Techniques for determining tissue characteristics using multiplexed immunofluorescence imaging

ABSTRACT

Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include obtaining at least one MxIF image of a same tissue sample, obtaining information indicative of locations of cells in the at least one MxIF image, identifying multiple groups of cells in the at least one MxIF image at least in part by determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image and grouping the at least some of the cells into the multiple groups using the determined feature values, and determining at least one characteristic of the tissue sample using the multiple cell groups.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application Ser. No. 62/986,010, filed Mar. 6, 2020,entitled “DETERMINING TISSUE CHARACTERISTICS USING MULTIPLEXEDIMMUNOFLUORESCENCE IMAGING,” the entire contents of which isincorporated herein by reference in its entirety.

FIELD

Aspects of the technology described herein relate to machine learningtechniques for image processing. In particular, some embodiments of thetechnology described herein relate to using neural network techniquesfor processing one or more multiplexed immunofluorescence images.

BACKGROUND

Multiplexed immunofluorescence (MxIF) imaging is a technique for imagingmultiple fluorescent cellular and/or histological markers in a singlebiological sample (e.g., a tissue sample). MxIF imaging involvesrepeated rounds of staining, imaging, dye chemical inactivation (e.g.,bleaching), and re-imaging to layer multiple fluorescent markers ontothe same regions of interest in the biological sample. The markers'fluorescence is then used to form images. MxIF imaging allows forimaging of multiple different markers (e.g., between 30 and 100 markers)for a single tissue sample, allowing for more information to be gleanedfrom a single cut of tissue.

Different types of markers may be used as part of MxIF imaging includingmembrane, cytoplasm, and nuclear markers that bind in membrane,cytoplasm and nuclear regions of cells, respectively. The resultingimages therefore allow for tissue analysis at a sub-cellular level.

SUMMARY

Some embodiments provide for a method, comprising using at least onecomputer hardware processor to perform: obtaining at least onemultiplexed immunofluorescence (MxIF) image of a same tissue sample;obtaining information indicative of locations of cells in the at leastone MxIF image; identifying multiple groups of cells in the at least oneMxIF image at least in part by: determining feature values for at leastsome of the cells using the at least one MxIF image and the informationindicative of locations of cells in the at least one MxIF image; andgrouping the at least some of the cells into the multiple groups usingthe determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.

Some embodiments provide for a system, comprising at least one computerhardware processor and at least one non-transitory computer-readablestorage medium storing processor-executable instructions that, whenexecuted by the at least one computer hardware processor, cause the atleast one computer hardware processor to perform: obtaining at least onemultiplexed immunofluorescence (MxIF) image of a same tissue sample;obtaining information indicative of locations of cells in the at leastone MxIF image; identifying multiple groups of cells in the at least oneMxIF image at least in part by: determining feature values for at leastsome of the cells using the at least one MxIF image and the informationindicative of locations of cells in the at least one MxIF image; andgrouping the at least some of the cells into the multiple groups usingthe determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.

Some embodiments provide for at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining at least one multiplexed immunofluorescence (MxIF)image of a same tissue sample; obtaining information indicative oflocations of cells in the at least one MxIF image; identifying multiplegroups of cells in the at least one MxIF image at least in part by:determining feature values for at least some of the cells using the atleast one MxIF image and the information indicative of locations ofcells in the at least one MxIF image; and grouping the at least some ofthe cells into the multiple groups using the determined feature values;and determining at least one characteristic of the tissue sample usingthe multiple groups.

Some embodiments provide for a method, comprising using at least onecomputer hardware processor to perform: obtaining at least onemultiplexed immunofluorescence (MxIF) image of a tissue sample;obtaining information indicative of a location of at least one cell inthe at least one MxIF image; determining a marker expression signaturefor the cell in the tissue sample based on a plurality of markersexpressed in the at least one MxIF image and the information indicativeof the location of the cell in the at least one MxIF image; andcomparing the marker expression signature to cell typing data thatcomprises at least one marker expression signature for a plurality ofdifferent types of cells to determine a cell type for the cell.

Some embodiments provide for a system comprising at least one computerhardware processor and at least one non-transitory computer-readablestorage medium storing processor-executable instructions that, whenexecuted by the at least one computer hardware processor, cause the atleast one computer hardware processor to perform: obtaining at least oneMxIF image of a tissue sample; obtaining information indicative of alocation of at least one cell in the at least one MxIF image;determining a marker expression signature for the cell in the tissuesample based on a plurality of markers expressed in the at least oneMxIF image and the information indicative of the location of the cell inthe at least one MxIF image; and comparing the marker expressionsignature to cell typing data that comprises at least one markerexpression signature for a plurality of different types of cells todetermine a cell type for the cell.

Some embodiments provide for at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining at least one MxIF image of a tissue sample; obtaininginformation indicative of a location of at least one cell in the atleast one MxIF image; determining a marker expression signature for thecell in the tissue sample based on a plurality of markers expressed inthe at least one MxIF image and the information indicative of thelocation of the cell in the at least one MxIF image; and comparing themarker expression signature to cell typing data that comprises at leastone marker expression signature for a plurality of different types ofcells to determine a cell type for the cell.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects and embodiments will be described with reference to thefollowing figures. It should be appreciated that the figures are notnecessarily drawn to scale. Items appearing in multiple figures areindicated by the same or a similar reference number in all the figuresin which they appear.

FIG. 1 is a diagram pictorially illustrating an exemplary system formultiplexed immunofluorescence (MxIF) image processing, according tosome embodiments of the technology described herein.

FIG. 2 is a diagram showing exemplary MxIF images and related data thatcan be generated by processing the MxIF images, according to someembodiments of the technology described herein.

FIG. 3 is a diagram showing exemplary components of a pipeline forprocessing MxIF images of a tissue sample to determine characteristicsof cells in the tissue sample, according to some embodiments of thetechnology described herein.

FIG. 4A is a diagram showing an exemplary processing flow of MxIF imagesusing some of the components of FIG. 3, according to some embodiments ofthe technology described herein.

FIG. 4B is a diagram showing the exemplary processing flow of MxIFimages of FIG. 4A including an optional tissue degradation checkcomponent, according to some embodiments of the technology describedherein

FIG. 4C is a diagram showing a patch mask generated based on tissuedegradation information determined by comparing two nucleus markerimages of the same tissue sample, according to some embodiments of thetechnology described herein.

FIG. 4D is a diagram showing use of the patch mask from FIG. 4C tofilter areas of a tissue sample for processing, according to someembodiments.

FIG. 5A is a flow chart showing an exemplary computerized process forprocessing MxIF images of a tissue sample based on cell location data togroup cells of the tissue sample to determine at least onecharacteristic of the tissue, according to some embodiments of thetechnology described herein.

FIG. 5B shows an example of feature values for an associated celllocation of cell location data, according to some embodiments of thetechnology described herein.

FIG. 6A is a flow chart showing an exemplary computerized process forprocessing MxIF images of a tissue sample based on cell location data ofa cell in the tissue sample to predict a type of the cell, according tosome embodiments of the technology described herein.

FIG. 6B show examples of immunofluorescence images and cell locationdata used for cell typing, according to some embodiments.

FIG. 6C shows an example of using a neural network to generateexpression data that is compared to cell typing data to determine apredicted cell type, according to some embodiments of the technologydescribed herein.

FIGS. 6D-6E show examples of using a neural network to generate aprobability table that is compared to a cell typing table to determine apredicted cell type, according to some embodiments of the technologydescribed herein.

FIG. 7A is a flow chart showing an exemplary computerized process forprocessing MxIF images of a tissue sample based on cell location data tocluster the cells of the tissue sample into multiple cell groups,according to some embodiments of the technology described herein.

FIG. 7B is a flow chart showing an exemplary computerized process forprocessing a first set of cell features (local cell features) using agraph neural network to identify one or more communities of cells,according to some embodiments of the technology described herein.

FIG. 7C shows examples of an image of tissue contours shaded based oncell types and an image of the same tissue with the contours shadedbased on cell clusters, according to some embodiments.

FIG. 8 shows an exemplary MxIF image and manual celllocation/segmentation data for the MxIF image, according to someembodiments of the technology described herein.

FIG. 9 shows examples of images that can be used to train a neuralnetwork to identify cell location information, according to someembodiments of the technology described herein.

FIG. 10 is a diagram pictorially illustrating an example of using aconvolutional neural network model to process MxIF images obtained of atumor to generate cell segmentation data, according to some embodimentsof the technology described herein.

FIG. 11 is a diagram pictorially illustrating another exemplary use of aneural network to process immunofluorescence images to generate celllocation/segmentation data, according to some embodiments of thetechnology described herein.

FIG. 12 shows a MxIF image and cell segmentation data generated based onthe MxIF image, according to some embodiments of the technologydescribed herein.

FIG. 13 shows a composite fluorescence image and cell segmentation datagenerated based on the composite fluorescence image, according to someembodiments of the technology described herein.

FIG. 14 is a diagram showing exemplary cell segmentation data forexemplary MxIF images obtained of kidney tissue, according to someembodiments of the technology described herein.

FIG. 15 shows a clear cell renal cell carcinoma (CCRCC) MxIF image andcorresponding cell segmentation data, according to some embodiments ofthe technology described herein.

FIG. 16 shows a CCRCC MxIF image and corresponding cell segmentationdata, according to some embodiments of the technology described herein.

FIG. 17 is a diagram of a convolutional network architecture forpredicting a noise subtraction threshold for subtracting noise from araw immunofluorescence image, according to some embodiments of thetechnology described herein.

FIG. 18 is a diagram illustrating exemplary tissue characteristics thatcan be determined by processing MxIF images, according to someembodiments of the technology described herein.

FIG. 19A is a diagram of a stroma mask and an acini mask generated byprocessing an immunofluorescence image, according to some embodiments ofthe technology described herein.

FIG. 19B is a diagram of using features of a tissue sample to generatean object mask, according to some embodiments of the technologydescribed herein.

FIG. 20 is a diagram showing examples of measuring acini shape, area andperimeter, according to some embodiments of the technology describedherein.

FIG. 21 is a diagram showing examples of spatial distributioncharacteristics, according to some embodiments of the technologydescribed herein.

FIG. 22 is a diagram showing examples of spatial organizationcharacteristics, according to some embodiments of the technologydescribed herein.

FIG. 23 is a diagram showing an example of cell contact information forimmunofluorescence images for two different patients, according to someembodiments of the technology described herein.

FIG. 24 is a diagram showing examples of information regarding cellneighbor information for the two different patients of FIG. 23,according to some embodiments of the technology described herein.

FIG. 25 shows two examples of MxIF images and corresponding stromalsegmentation masks, according to some embodiments of the technologydescribed herein.

FIG. 26 shows exemplary MxIF images, segmentation masks, andcorresponding cell groups, according to some embodiments of thetechnology described herein.

FIG. 27 shows an example of a full MxIF slide processing to generatecell groups, according to some embodiments of the technology describedherein.

FIG. 28 is a diagram of a restored cell arrangement generated byprocessing an immunofluorescence image, according to some embodiments ofthe technology described herein.

FIG. 29 shows a 4′,6-diamidino-2-phenylindole (DAPI) stainedimmunofluorescence image and two images of different cell groups for theDAPI image, according to some embodiments of the technology describedherein.

FIG. 30 shows cell groups for different CCRCC tissue samples, accordingto some embodiments of the technology described herein.

FIG. 31 is a diagram showing exemplary cell groups for the exemplaryMxIF images obtained of kidney tissue from FIG. 14, according to someembodiments of the technology described herein.

FIG. 32 shows a set of images of cell groups for different clear cellrenal cell carcinoma (CCRCC) tissue samples, according to someembodiments of the technology described herein.

FIG. 33 shows a series of images of cell groups for different CCRCCtissue samples, according to some embodiments of the technologydescribed herein.

FIG. 34 is a diagram showing an analysis of two different MxIF images ofCCRCC tissue samples, according to some embodiments of the technologydescribed herein.

FIG. 35 is a diagram showing an analysis of a MxIF image of a CCRCCtissue sample, according to some embodiments of the technology describedherein.

FIGS. 36A-B are diagrams illustrating cell quantities and proportions,according to some embodiments of the technology described herein.

FIG. 37 is a diagram illustrating cell distribution characteristics,according to some embodiments of the technology described herein.

FIGS. 38A-B are diagrams of percentage heatmaps and distributiondensities of histological features, according to some embodiments of thetechnology described herein.

FIG. 39 is a diagram showing cell neighbor information and cell contactcharacteristics, according to some embodiments of the technologydescribed herein.

FIG. 40 is a diagram showing examples of tSNE plots of characteristicsof marker expression, according to some embodiments of the technologydescribed herein.

FIG. 41 is another diagram illustrating showing examples of tSNE plotsof characteristics of marker expression, according to some embodimentsof the technology described herein.

FIG. 42 is a diagram pictorially illustrating use of a convolutionalneural network to determine cell segmentation data for a4′,6-diamidino-2-phenylindole (DAPI) univen stained immunofluorescenceimage, according to some embodiments of the technology described herein.

FIG. 43 is a diagram pictorially illustrating a first cell maskgenerated based on a combination of a DAPI stained immunofluorescenceimage and a CD3 cellular marker image of a tissue sample, according tosome embodiments of the technology described herein.

FIG. 44 is a diagram pictorially illustrating a second cell maskgenerated based on a combination of the DAPI stained immunofluorescenceimage of FIG. 43 and a CD21 cellular marker image, according to someembodiments of the technology described herein.

FIG. 45 is a diagram pictorially illustrating a third cell maskgenerated based on a combination of the DAPI stained immunofluorescenceimage of FIG. 43 and a CD11c cellular marker image, according to someembodiments of the technology described herein.

FIG. 46 is a diagram pictorially illustrating a blood vessel maskgenerated based on MxIF images of the tissue sample of FIG. 43,according to some embodiments of the technology described herein.

FIG. 47 is a diagram pictorially illustrating cell groups generatedusing the masks from FIGS. 43-46, according to some embodiments of thetechnology described herein.

FIG. 48 shows a set of cell groups for a prostate tissue sample and amalignant area, according to some embodiments of the technologydescribed herein.

FIG. 49 shows an enhanced view of a portion of the prostate tissuesample of FIG. 48, according to some embodiments of the technologydescribed herein.

FIG. 50 shows a set of cell groups for prostate tissue samples takenfrom four different patients, according to some embodiments of thetechnology described herein.

FIG. 51 is a diagram of an illustrative implementation of a computersystem that may be used in connection with any of the embodiments of thetechnology described herein.

FIGS. 52-53 show two exemplary comparisons of cell location informationgenerated using an implementation of the techniques described herein anda conventional technique, according to some embodiments.

DETAILED DESCRIPTION

The inventors have developed new image processing and machine learningtechniques for processing multiplexed images of biological samples suchas, for example, multiplexed immunofluorescence (MxIF) images of atissue sample of a person having, suspected of having, or at risk ofhaving cancer or another disease. Such images can be obtained, forexample, using labeled antibodies as labeling agents. Each antibody canbe labeled with various types of labels, such as fluorescent labels,chemical labels, enzymatic labels, and/or the like.

The techniques developed by the inventors provide robust informationregarding various medically-relevant characteristics of tissue samplesthat may characterize the cellular composition (e.g., cell type, cellmorphology etc.) and/or organization (e.g., cell localization,multi-cellular structure localization, etc.) of the tissue sample.Examples of tissue sample characteristics include, but are not limitedto, information about the types of cells in the tissue sample,morphological information, spatial information, and informationidentifying locations of certain tissue structures (e.g., acini, stroma,tumor, vessels, etc.).

The inventors have appreciated that it is desirable to be able toidentify different cell groups in a tissue sample that can be used todetermine medically-relevant characteristics of the tissue sample,including any of the characteristics described above. However,conventional techniques for processing MxIF images are unable todetermine cell groups accurately and automatically. In particular,conventional techniques are not fully automated and cannot analyze MxIFdata to accurately determine cell types, cell groupings and/or otherinformation of the tissue sample that can be used to determine tissuecharacteristics of interest. Therefore, conventional techniques requiremanual intervention for various step(s) of the MxIF image processingprocedure, including to identify cells and/or other aspects of thetissue sample, as well as to configure parameters of the semi-automatedpart of the process. Such manual intervention results in tediousanalysis that is consuming (e.g., since such images typically have alarge number of cells, hand-annotating the images can be a very timeconsuming and non-trivial task), and provide inconsistent results sincethe process is subject to human error and/or is not easily repeatableacross different samples (e.g., which can result in inconsistency inMxIF tissue analysis procedures and/or MxIF tissue analysis data).

The inventors have developed a novel image processing pipeline in whichinformation indicative of cell locations (e.g., information from whichcell boundaries can be derived, cell boundaries, and/or masks) can beused to determine feature values for individual cells based on pixelvalues for those cells in one or more MxIF images (e.g., by determiningone or more feature values for cells in the tissue sample). In turn, thefeature values may be used to group the cells into multiple groups,which can be used to determine the cell characteristics of interest. Insome embodiments, the feature values are indicative of how much eachchannel of the at least one MxIF image is expressed for a cell, whichcan therefore be used to determine a marker expression signature for thecells in the MxIF images.

In some embodiments, the cell groups can be determined by performing acell typing process that determines the types of cells in the tissuesample (e.g., which can be used to group the cells by cell type) basedon the feature values for the individual cells. In some embodiments, thefeature values can be determined based on traditional chemical stainingof different cellular and/or tissue structures. For example, in someembodiments, the feature values for the cells may include markerexpression signatures (e.g., a marker expression signature may becomputed for each of one or more cells) and the marker expressionsignatures may be used to determine the type of each cell (e.g., whetherthat cell is an acini cell, macrophage cell, myeloid cell, t-cell,b-cell, endothelium cell, and/or any other cell type). In turn, thecells may be grouped based on their cell types, such that each groupincludes cells of a particular type (e.g., a group containing acinicells, a group containing macrophage cells, and so on for the variouscell types of the cells of interest). Marker expression signatures aredescribed further herein. Additionally, or alternatively, the cellgroups can be determined based on feature values (e.g., where the sameand/or different cell types may be associated with a same group based onhaving similar feature values). For example, in some embodiments thefeature values for cells may include any information indicative of theexpression levels of various markers in the cells. For example, thefeature values for a particular cell may be determined using the pixelvalues for pixels at the location, in the at least one MxIF image, ofthe particular cell. For example, the feature values may be determinedby averaging or computing the median value of such pixels, which mayindicate a mean or median expression level of markers in the particularcell. In some embodiments, determining the one or more characteristicscan include determining the cell type of each of the multiple groups. Insome embodiments, the groups may represent different portions ofdifferent tissue structure(s) present in the tissue sample.

Accordingly, some embodiments provide for a computer-implemented methodcomprising: (1) obtaining at least one MxIF image of a same biologicalsample (e.g., a tissue sample from a subject having, suspected ofhaving, or at risk of having cancer); (2) obtaining informationindicative of locations of cells (e.g., cell boundaries, informationfrom which cell boundaries can be derived, and/or masks) in the MxIFimage (e.g., by applying a machine learning model to identify theboundaries of cells in the images, by computing cell locationinformation (e.g., a cell location mask) from the image and/or accessingsuch cell location information); (3) identifying multiple groups ofcells in the at least one MxIF image at least in part by: (a)determining feature values for at least some of the cells using the MxIFimage and the information indicative of locations of the at least someof the cells (e.g., computing feature values for a cell by computing anaverage or median pixel value of pixels within the boundary of that cellas indicated by the information indicative of the locations of the atleast some of the cells, computing feature values for a cell bycomputing a marker expression signature as described herein); and (b)grouping (e.g., grouping cells by type, group cells based on average ormedian pixel values using any suitable clustering algorithm, etc.) theat least some of the cells into the multiple groups using the determinedfeature values; and (4) determining at least one characteristic of thetissue sample using the multiple groups (e.g., determining the cell typeof each group, determining cell masks, determining cell communities,determining statistical information about distributions of the cells ofthe multiple groups, determining spatial distributions of cell types,determining morphological information, etc.).

In some embodiments, the information indicative of locations of cellscan be obtained from chemical staining of cells or cellular structures.In some embodiments, the chemical staining can produce a fluorescentsignal (e.g., DAPI). In some embodiments, labeled (e.g., fluorescentlylabeled) antibodies can be used to detect specific intracellular,membrane, and/or extracellular locations within a cell or tissue sample.In some embodiments, labeled antibodies alone can be used. In someembodiments, labeled antibodies can be used along with one or more otherstains (e.g., other fluorescent stains).

In some embodiments, obtaining the at least one MxIF image comprisesobtaining a single multi-channel image of the same tissue sample,wherein channels in the single multi-channel image are associated withrespective markers in a plurality of markers (e.g., any one or more ofthe markers described herein, including the markers discussed in thedetailed description section). In some embodiments, obtaining at leastone MxIF image comprises obtaining a plurality of immunofluorescenceimages of the same tissue sample. At least one of the plurality ofimmunofluorescence images can include a single-channel image associatedwith a respective marker. At least one of the plurality ofimmunofluorescence images can include a multi-channel image, whereinchannels in the multi-channel image are associated with respectivemarkers in a plurality of markers (e.g., any one or more of the markersdescribed herein, including the markers discussed in the detaileddescription section). In some embodiments, the at least one MxIF imageof the tissue sample is captured in vitro.

In some embodiments, feature values for a cell may include values of thecell's pixels in one or more of the MxIF images. For example, in someembodiments, the cells include a first cell and determining the featurevalues comprises determining first feature values for the first cellusing at least one pixel value associated with a location of the firstcell in the at least one MxIF image. In some embodiments, the at leastsome of the cells include a second cell and determining the featurevalues comprises determining second feature values for the second cellusing at least one pixel value associated with a location of the secondcell in the at least one MxIF image. In some embodiments, determiningthe first feature values for the first cell comprises using pixel valuesassociated with respective locations of the first cell in multiplechannels of the at least one MxIF image.

In some embodiments, using pixel values associated with respectivelocations of the first cell in multiple channels comprises, for each ofthe multiple channels: (a) identifying a set of pixels for the firstcell using information indicative of the location of the first cell inthe channel; and (b) determining a feature value for the first cellbased on values of pixels in the set of pixels. In some embodiments, theinformation indicative of the location of the first cell indicates thelocation of the first cell's boundary, and identifying the set of pixelsfor the first cell comprises identifying pixels at least partially(e.g., partially or fully) within the first cell's boundary.

In some embodiments, determining the first feature values comprisesdetermining a feature value for one or more (e.g., at least one, atleast three, at least five, at least ten, at least fifteen, between oneand ten, between five and twenty, or any other suitable number, range,or value within a range) of the following markers of the at least oneMxIF image: ALK, BAP1, BCL2, BCL6, CAIX, CCASP3, CD10, CD106, CD11b,CD11c, CD138, CD14, CD16, CD163, CD1, CD1c, CD19, CD2, CD20, CD206,CD209, CD21, CD23, CD25, CD27, CD3, CD3D, CD31, CD33, CD34, CD35, CD38,CD39, CD4, CD43, CD44, CD45, CD49a, CD5, CD56, CD57, CD66b, CD68, CD69,CD7, CD8, CD8A, CD94, CDK1, CDX2, Clec9a, Chromogranin, Collagen IV,CK7, CK20, CXCL13, DAPI, DC-SIGN, Desmin, EGFR, ER, ERKP, Fibronectin,FOXP3, GATA3, GRB, GranzymeB, H3K36TM, HER2, HLA-DR, ICOS, IFNg, IgD,IgM, IRF4, Ki67, KIR, Lumican, Lyve-1, Mammaglobin, MHCI, p53,NaKATPase, PanCK, PAX8, PCK26, CNAP, PBRM1, PD1, PDL1, Perlecan, PR,PTEN, RUNX3, S6, S6P, SMA, SMAa, SPARC, STAT3P, TGFb, Va7.2, andVimentin.

In some embodiments, multiple markers can be present and/or detected ina single channel. For example, the signal from a chemical stain (e.g.,DAPI) can be in the same image of the tissue sample (or channel) that isalso used to provide immunofluorescent signals (e.g., to detect thelocation information of cells). In some embodiments, only one marker ispresent in a single channel and/or image.

In some embodiments, grouping the cells into multiple cell groupscomprises clustering cells based on their respective feature valuesusing a clustering algorithm. Any suitable clustering algorithm may beused including, for example, a centroid-based clustering algorithm(e.g., K-means), a distribution based clustering algorithm (e.g.,clustering using Gaussian mixture models), a density-based clusteringalgorithm (e.g., DBSCAN), a hierarchical clustering algorithm, principalcomponents analysis (PCA), independent components analysis (ICA), and/orany other suitable clustering algorithm, as aspects of the technologydescribed herein are not limited in this respect.

In some embodiments, grouping the at least some of the cells into themultiple groups comprises analyzing the determined feature values todetermine relationships among the at least some of the cells, anddetermining the multiple groups based on the determined relationshipssuch that each cell in a group of the multiple groups has feature valuesthat are indicative of a relationship among cells in the group. In someembodiments, determining relationships among the at least some of thecells comprises determining similarities among feature values of the atleast some of the cells. In some embodiments, determining relationshipsamong the at least some of the cells comprises comparing the featurevalues to known cell typing data to determine a cell type for each ofthe at least some of the cells.

In some embodiments, the techniques include performing cell typing basedon the feature values of the individual cells. In some embodiments, thefeature values can include channel contributions (e.g., mean channelcontributions indicative of how much each channel contributes to a cell)that can be used to determine cell types and/or cell groups (e.g., cellclusters). The inventors have appreciated that while mean channelcontributions can be used for cell typing, in some instances, leveragingmean channel contribution in cells can be affected by the quality ofsegmentation, the size of the cells, the shape of the cells, and/or theconditions of the tissue staining. For example, such techniques can beprone to variations in marker intensity, can add marker expression(s)from nearby cell contours (e.g., due to segmentation errors), and/or cancomplicate the cell type identification process by creating additionalclusters of cells with intermediate marker expressions. As anotherexample, the inventors have appreciated that channel contributions donot take into account information about intracellular signallocalization (e.g., which can be useful to help distinguish real signaldata from noise). As a further example, the inventors have alsoappreciated that without an ability to set the range of values ofchannel contributions for specific types of cells, it can be difficultto purposefully search for cell types of interest (e.g., instead,clustering can be used, which may not find cells of interest).Therefore, the inventors have appreciated that mean channelcontributions may not provide for stable cell typing. Given suchpotential problems, cell typing results may need to be checked manually,which can increase delays and impact automation.

The inventors have therefore developed techniques for cell typing thatutilize machine learning. In some embodiments, cell typing may beperformed by using a trained neural network to determine a markerexpression signature for each of one or more cells. The markerexpression signature for a cell may include, for each particular markerof one or more of a plurality of markers, a likelihood that theparticular marker is expressed in the cell. In turn, the markerexpression signature for the cell may be used to identify the type ofthe cell (e.g., by comparing the marker expression signature withpreviously-determined marker expression signatures that have beenassociated, for example by pathologists, with respective cell types).Such machine learning techniques provide for an automated way of celltyping (e.g., as compared to conventional techniques, which may requirea user to manually adjust settings). In some embodiments, the trainedneural network may take as input at least one MxIF image with one ormore channels (e.g., separate one-channel MxIF images, a two-channelMxIF image, a three-channel MxIF image, and/or the like) and celllocation data for the cell of interest. The trained neural network canuse not only marker expression intensity, but also other data such asdetected cell shape, cell texture and the location of the markerexpression. The neural network can output the likelihood of the cellhaving a signal of proper intensity and shape (e.g., in the 0 to 1range) for each channel and its associated marker. The likelihoodsdetermined by the neural network for each marker can be combined togenerate the marker expression signature for the cell of interest, whichcan be compared to cell typing data to determine a predicted cell typefor the cell of interest. Due to the robustness of the trained neuralnetwork to signal level distribution differences (e.g., which isachieved by training the network using heterogeneous training data, andleveraging additional features as described both above and herein), thecell typing approach described herein provides for automated cell typedetection that provides for robust signal presence decisions for eachcell.

In some embodiments, the multiple channels are associated withrespective markers in a plurality of markers, determining the firstfeature values using the pixel values associated with the respectivelocations of the first cell in the multiple channels of the at least oneMxIF image comprises determining a marker expression signature thatincludes, for each particular marker of one or more of the plurality ofmarkers, a likelihood that the particular marker is expressed in thefirst cell, and grouping the at least some of the cells into themultiple groups comprises determining a predicted cell type for thefirst cell using the marker expression signature and cell typing data,and associating the first cell with one of the multiple groups based onthe predicted cell type. In some embodiments, determining the featurevalues for the at least some of the cells comprises determining a markerexpression signature, for each particular cell of multiple cells in theat least one MxIF image, the marker expression signature including, foreach particular marker of one or more of a plurality of markers, alikelihood that the particular marker is expressed in the particularcell, grouping the at least some of the cells into the multiple groupsusing the determined feature values comprises determining a predictedcell type for each particular cell of the multiple cells using themarker expression signature for the particular cell and cell typingdata, and grouping the multiple cells into the multiple groups based onthe predicted cell type. The plurality of markers comprises at least oneof the markers described herein.

In some embodiments, the cell typing data comprises at least one markerexpression signature for each of a plurality of cell types, and the atleast one marker expression signature for each particular cell type ofthe plurality of cell types comprises data indicative of which of theplurality of markers is expressed in cells of the particular cell type.

In some embodiments, the method further comprises determining the markerexpression signature using a first trained neural network configured todetermine marker expression signatures. In some embodiments, the firsttrained neural network comprises at least one million parameters. Insome embodiments, the method further includes training the first trainedneural network using a set of training immunofluorescence images oftissue samples for an associated cell type as input images andassociated output data comprising information indicative of markerexpressions in the input images for the associated cell type. In someembodiments, the method comprises providing the at least one MxIF imageto the first trained neural network as input and obtaining the markerexpression signature as output from the first trained neural network.

In some embodiments, the method includes providing, from the informationindicative of the locations of the cells, information indicative of alocation of the first cell in at least some of the multiple channels aspart of the input to the first trained neural network. The trainedneural network can include a plurality of convolutional layers.

In some embodiments, the cell typing data comprises multiple markerexpression signatures including at least one marker expression signaturefor each of a plurality of cell types, and determining the predictedcell type of the first cell comprises comparing the marker expressionsignature of the first cell with at least one marker expressionsignature among the multiple marker expression signatures to determinethe predicted cell type. In some embodiments, comparing the markerexpression signature of the first cell with at the at least one markerexpression signature among the multiple marker expression signatures isperformed using a measure of distance. In some embodiments, the measureof distance is at least one of a cosine distance, a Euclidian distance,and a Manhattan distance. In some embodiments, the method furtherincludes determining the predicted cell type by selecting the comparisonmetric of the computed comparison metrics with a lowest value or ahighest value.

In some embodiments, the at least one characteristic of the tissuesample characterizes a cellular composition of the tissue sample, anorganization of the tissue sample, or both. In some embodiments,determining the at least one characteristic comprises determininginformation about cell types in the tissue sample. For example, in someembodiments, determining information about cell types in the tissuesample comprises identifying one or more cell types present in thetissue sample. In some embodiments, the cell types comprise one or moreof endothelial cells, epithelial cells, macrophages, T cells, malignantcells, NK cells, B cells, and acini cells. In some embodiments, the Tcells comprise one or more of CD3+ T cells, CD4+ T cells, and CD8+ Tcells. In some embodiments, determining information about cell types inthe tissue sample comprises determining a percentage of one or more celltypes in the tissue sample. In some embodiments, the cell types can bedetermined using various information and/or techniques. In someembodiments, cell type can be determined histologically based on theirsize, shape, and/or staining (e.g., using different chemical stains).Additionally or alternatively, immunofluorescent signals can be used toevaluate the size and shape of cells to determine cell types.Additionally or alternatively, cell-specific markers (e.g., proteins)can be used (e.g., by using labeled antibodies) to determine cell types.

Any of a plurality of numerous types of tissue characteristics may bedetermined using the techniques developed by the inventors and describedherein. For example, in some embodiments, determining the at least onecharacteristic comprises determining statistical information about adistribution of at least a portion of cells of the multiple groups ofcells. For example, in some embodiments, determining statisticalinformation about the distribution of at least some cells of themultiple cell types comprises determining spatial distributions of oneor more cell types in the tissue sample. In some examples, determiningstatistical information about the distribution of at least some of thecells comprises determining distributions between different cell types.

As another example, in some embodiments, determining the at least onecharacteristic comprises determining spatial information about locationsof at least some cells of the multiple groups of cells. In someembodiments, determining the spatial information comprises determiningdistances between cells of the multiple groups of cells. In someembodiments, determining the spatial information comprises determiningone or more areas of the tissue sample that include one or more cells ofa group of one of the multiple groups of cells. In some examples,determining spatial information about locations of at least some cellscomprises determining a spatial organization of one or more cell typesof the tissue sample (e.g., including information about cellorganization in the tissue sample by cell type, compared to other celltypes, and/or the like). In some examples, determining spatialinformation about locations of at least some cells comprises determiningone or more areas of the tissue sample that comprise one or more celltypes (e.g., areas that include a number of cells of one or more celltypes above a threshold).

As another example, in some embodiments, determining the at least onecharacteristic comprises determining morphological information about atleast some cells of the multiple groups of cells. In some examples,determining morphological information about at least some cellscomprises determining information about the form and/or structure of thecells and/or of the tissue sample (e.g., as grouped in the multiplegroups), such as the shape, structure, form, and/or size of the cells.

As another example, in some embodiments, determining the at least onecharacteristic comprises determining physical information for at leastsome cells of the multiple groups of cells (e.g., for cells of onegroup, cells of multiple groups, etc.), the physical informationcomprising at least one of a cell area, a cell perimeter, a cell size.

In some embodiments, the determined characteristics can includemorphological information, spatial information, locations of tissuestructures, and/or the like. The inventors have appreciated that suchcharacteristics can be determined by creating one or more masks that canbe used to analyze the cellular structure of the tissue sample, theanalysis of which can in-turn be used to determine the characteristic(s)of interest. However, as noted above, typically such masks must becreated manually (e.g., using signal thresholding) since computerizedtechniques are unable to automatically identify cells and/or cell groupsin the tissue sample. By leveraging feature values to automaticallydetermine the cell information, such as cell segments and/or cellgroups, such masks can be used to analyze different cells and/or cellstructures in the tissue sample that can allow the system to determinethe characteristics of the tissue sample. For example, the techniquescan identify a cell type in the tissue sample (e.g., T cells) and use astromal mask to identify whether the T cells are in stromal areas and/ornon-stromal areas of the tissue sample.

In some embodiments, a mask may be a binary mask. The binary mask may bea pixel-level binary mask including a 0 or a 1 (or any other suitabletype of binary value) for at least some of the pixels in an MxIF image.

In some embodiments, determining the at least one characteristic maycomprise determining one or more acini masks indicating locations ofacini in the at least one multiplexed immunofluorescence image of thetissue sample. In some embodiments, an acini mask for an MxIF image maybe a binary mask, and may include binary values for at least some of thepixels in the MxIF image, with a binary value for a pixel indicatingwhether or not the pixel is located in acini shown in the MxIF image.

As another example, in some embodiments, determining the at least onecharacteristic comprises determining one or more stromal masksindicating locations of stroma in the at least one MxIF image of thetissue sample. In some embodiments, a stromal mask for an MxIF image maybe a binary mask, and may include binary values for at least some of thepixels in the MxIF image, with a binary value for a pixel indicatingwhether or not the pixel is located in stroma shown in the MxIF image.

As another example, determining the at least one characteristiccomprises determining one or more tumor masks indicating locations of atumor in the at least one MxIF image of the tissue sample. In someembodiments, a tumor mask for an MxIF image may be a binary mask, andmay include binary values for at least some of the pixels in the MxIFimage, with a binary value for a pixel indicating whether or not thepixel is located in a tumor shown in the MxIF image.

The inventors have further appreciated that it can be desirable tosearch for certain cell structures in a tissue sample, such as a cellstructure indicative of a cancer (e.g., breast cancer, renal carcinoma,etc.). Some conventional approaches for performing cell clustering intocommunities use information from neighborhoods in reconstructed cellcontact graphs (e.g., as described in Yury Goltsev et al., “DeepProfiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging,”PMID: 30078711 (August, 2018), available atpubmed.ncbi.nlm.nih.gov/30078711/). However such approaches do notprovide for incorporating other information that the inventors havediscovered and appreciated can be relevant to the clustering process.

The inventors have developed techniques for identifying clusters orcommunities of cells using a graph neural network that leverage the cellfeatures. The cell features can include, for each cell, a cell type,cell neighbors, neighboring cell types, neighbor distance data, and/orother data as described further herein. The techniques can includeidentifying cell communities in a tissue sample by leveraging such cellfeatures, and identifying information of those cells, such as celltypes, distances (e.g., to provide information regarding sparselypopulated cell clusters, close cell clusters, etc.). Such masks cantherefore provide for discovering complex structures in tissues, whichare difficult to detect otherwise (e.g., compared to using thresholdingapproaches), and can provide for automatic clustering at scale. Someembodiments apply a graph neural network to perform cell cluster orcommunity detection.

In some embodiments, determining the at least one characteristiccomprises determining one or more cell clusters of the tissue samplebased on the multiple groups, and determining a cell cluster maskindicating locations of one or more cell clusters of the tissue sample.In some embodiments, the one or more cell clusters are determined bygenerating a first set of cell features (e.g., by triangulating theimages to generate a graph, which is used to determine at least some ofthe cell features, such as cell neighbors, cell neighbor distances,etc.), embedding the first set of cell features into a higherdimensional space using a trained graph neural network, and identifyingcommunities of cells by clustering the embedded features.

In some embodiments, determining the one or more cell clusters in thetissue sample comprises generating a graph comprising a node for each ofthe at least some cells, and edges between the nodes determiningfeatures for nodes in the graph, providing the features for the nodes inthe graph as input to a graph neural network to obtain embedded featuresin a latent space, clustering the embedded features to obtain clustersof the nodes, and using the clusters of the nodes to determine the oneor more cell clusters.

In some embodiments, each cell cluster of the one or more cell clusterscomprises a plurality of cell types. In some embodiments, each cellcluster represents at least a part of a tissue structure of the tissuesample. In some embodiments, the tissue structure comprises mantletissue, stromal tissue, a tumor, a follicle, a blood vessel, or somecombination thereof.

In some embodiments, the method comprises determining the one or morecell clusters based on a first set of cell features of the at least somecells. The method can further include determining the first set of cellfeatures for each cell by generating a graph comprising a node for eachof the at least some cells in the tissue sample, and edges between thenodes. In some embodiments, the method further comprises generating thegraph based on the at least some cells of the tissue sample usingtriangulation (e.g., Delanuay triangulation and/or any other type oftriangulation).

In some embodiments, the method further comprises determining the firstset of cell features for each node of the graph based on a group of themultiple groups that includes the cell, lengths of edges of the node inthe graph, and mask data. In some embodiments, the method furthercomprises encoding the graph into a sparse adjacency matrix. In someembodiments, the method further comprises encoding the graph into anadjacency list of the edges of the graph.

In some embodiments, the method further comprises providing the graph asan input to a trained graph neural network, and obtaining from the graphneural network a set of feature embeddings for each node. In someembodiments, the trained graph neural network comprises one or moreconvolutional layers. In some embodiments, the set of feature embeddingsare generated based on activations of a last graph convolutional layerof the trained graph neural network. In some embodiments, determiningthe one or more cell clusters comprises determining the one or more cellclusters based on the set of feature embeddings of each node. In someembodiments, determining the one or more cell clusters based on the setof feature embeddings of each node comprises clustering the cells in thetissue sample based on the set of feature embeddings of each node.

In some embodiments, the MxIF images may be preprocessed before featurevalues for cells are determined. In some embodiments, machine learningtechniques are applied to preprocess MxIF images to remove artifactsand/or to identify information indicative of the cell locations. Theinventors have appreciated that MxIF images may have artefactsintroduced during imaging such as, for example, noise generated by themicroscope imaging the tissue, noise from surrounding cells (e.g.,fluorescence noise) in the tissue sample, noise from antibodies in thetissue sample, and/or any other types of artefacts that may be presentin MxIF images. For example, a low signal to noise level can impact cellclustering, and therefore the raw MxIF images and/or the data generatedby the cell segmentation process may not be sufficient for automatedcell clustering. The inventors have therefore developed techniques toprocessing the immunofluorescence images to remove artifacts, such as byperforming background subtraction to remove noise.

Accordingly, in some embodiments, background subtraction may beperformed on one or more of the MxIF images. Accordingly, in someembodiments, obtaining the at least one MxIF image of the tissue samplecomprises performing background subtraction on a first channel of the atleast one MxIF image. In some embodiments, performing the backgroundsubtraction comprises providing the first channel as input to a secondtrained neural network model (e.g., a convolutional neural network, aconvolutional neural network having a U-net architecture) configured toperform background subtraction. In some embodiments, the second trainedneural network comprises at least one million parameters. In someembodiments, the method further includes training the second trainedneural network using a set of training immunofluorescence imagesincluding noise as input images and associated output data comprisingassociated images without at least some noise. It should be appreciated,however, that preprocessing of MxIF images may include other types ofpre-processing (in some embodiments) in addition to or (in someembodiments) instead of background subtraction, as aspects of thetechnology described herein are not limited in this respect. Forexample, preprocessing may include filtering, noise suppression,artefact removal, smoothing, sharpening, transforming to a differentdomain (e.g., wavelet domain, Fourier domain, short-time Fourier domain)to perform a preprocessing operation and transforming back to the imagedomain, and/or any other suitable type of preprocessing. In someembodiments, the techniques include removing noise from particularregions in the image, since some regions may exhibit noise more thanother regions. Additionally or alternatively, in some embodiments theimages can be processed on a per-channel basis (e.g., to remove noise ona per-marker basis, since different markers may exhibit differentnoise). In some embodiments, a trained machine learning model processesthe immunofluorescence images to perform the background subtraction bythresholding the immunofluorescence images.

The inventors have further appreciated that since the MxIF imageacquisition process requires repeatedly staining the same image withdifferent markers, each time a marker is washed out of the tissuesample, local tissue damage can occur. As a result, individual cellsand/or groups of cells could be damaged, such as being washed out and/orshifted relative to the original location in the sample. Using suchunintentionally modified portions of the tissue sample can causeundesired effects in the image processing pipeline. For example, suchdamage can result in incorrect information for the damaged cells beingincorporated into the feature values, cell groupings and/or determinedcharacteristics. If performed at all, conventional techniques requiremanual tissue checks. The inventors have therefore developed anautomated tissue degradation check for immunofluorescence images (e.g.,by comparing nuclei markers obtained across multiple staining steps) tocheck for and/or identify area(s) of tissue damage. The identifiedarea(s) can be ignored by subsequent steps (e.g., such as stepsperformed by the cell typing component 340, the cell morphologyassessment component 350, and/or the characteristic determinationcomponent 360 of the image processing pipeline 300 shown in FIG. 3) toensure that only undamaged portions of the tissue sample are analyzed bythe image processing pipeline. For example, a mask can be used to skipdamaged areas from processing, which can allow for tissue samples withsevere damage to be processed using the techniques described herein(compared to conventional techniques, which would not be able to processsuch damaged tissue samples).

In some embodiments, the at least one MxIF image comprises a pluralityof immunofluorescence images, and obtaining the information indicativeof the locations of the cells in the at least one MxIF image comprisesanalyzing the plurality of immunofluorescence images to identify one ormore damaged portions of the tissue sample.

In some embodiments, analyzing the plurality of immunofluorescenceimages comprises processing the plurality of immunofluorescence imagesusing a third trained neural network configured to identify differencesbetween immunofluorescence images. In some embodiments, the methodfurther includes training the third trained neural network using a setof training immunofluorescence images comprising pairs ofimmunofluorescence images of the same marker as input images andassociated output data comprising information indicative of whether atleast a portion of the tissue sample is damaged.

In some embodiments, the method further comprises inputting acorresponding portion of each of at least two of the plurality ofimmunofluorescence images to the third trained neural network, whereinthe at least two immunofluorescence images comprise images of a samemarker, and obtaining from the third trained neural network at least oneclassification of the portion of the tissue sample. In some embodiments,the at least one classification comprises a set of classifications. Insome embodiments, the set of classifications comprises a firstclassification of no cells, a second classification of undamaged cells,a third classification of damaged cells, or some combination thereof. Insome embodiments, the set of classifications comprises, for eachclassification, an associated confidence of the classification applyingto the portion, and selecting, based on the confidences, a finalclassification from the set of classifications for the portion.

In some embodiments, the method includes generating a patch maskindicating the final classification of the portion and finalclassifications for a plurality of other portions of the at least twoimmunofluorescence images. In some embodiments, the method furtherincludes removing, based on the patch mask, a portion of a segmentationmask for the tissue sample so that cells of the tissue sample associatedwith the removed portion are not included in the multiple cell groups.

In some embodiments, the third trained neural network comprises one ormore convolutional layers. In some embodiments, the third trained neuralnetwork comprises at least five million parameters. In some embodiments,the trained neural network is configured to output a confidence thatdifferent portions of the tissue sample comprise the at least oneclassification. In some embodiments, the at least one classificationcomprises a set of classifications. In some embodiments, the set ofclassifications comprise a first classification of no cells, a secondclassification of undamaged cells, a third classification of damagedcells, or some combination thereof.

The inventors have appreciated deficiencies with conventional cellsegmentation approaches used to process MxIF images. To process MxIFimages, conventional techniques typically perform cell segmentationbased on nuclei thresholding, and require user to manually adjustsettings for different images and/or to identify cell membranes (e.g.,by having the user expand each nuclei by outlining the nuclei with adesired number of pixels). Examples of such conventional approachesinclude CellProfiler (which discussed further below with reference toFIGS. 52-53 to demonstrate improvements of the cell segmentationtechniques described herein compared to CellProfiler), Ilastik (e.g.,described in Stuart Berg et al., “ilastik: interactive machine learningfor (bio)image analysis,” (September 2019), available atwww.nature.com/articles/s41592-019-0582-9), and QuPath (e.g., describedin Peter Bankhead et al., “QuPath: Open source software for digitalpathology image analysis,” (Dec. 2017), available atwww.nature.com/articles/s41598-017-17204-5). Some conventionalapproaches may use neural networks, but the use of such neural networksis typically limited to nuclei detection, and therefore conventionalapproaches do not use neural networks to determine cell segmentationinformation. As a result, the inventors have appreciated thatconventional cell segmentation techniques suffer from variousdeficiencies, including requiring manual user input and producing poorand inconsistent cell segmentation results (which can have furtherdownstream implications for step(s) that leverage the cell segmentationdata, such as cell typing, cell morphology assessment, andcharacteristic determination).

Some embodiments include executing a trained neural network to generatecell segmentation data. The trained neural network is tailored to MxIFimages, including by training the network using images with differentdensities of cells. As a result, the techniques do not require users tomanually adjust settings for individual immunofluorescence images sincethe trained model can be robust to signal perturbations. Such techniquescan therefore provide for processing large volumes of immunofluorescenceimages quickly (e.g., compared to conventional techniques, which requiremanual adjustment and curation for each step).

In some embodiments, obtaining the information indicative of thelocations of the cells in the at least one MxIF image comprises applyinga fourth trained neural network to at least one channel of the at leastone MxIF image to generate the information indicative of the locationsof the cells. For example, in some embodiments, a neural network model(e.g., a convolutional neural network) may be applied to identify cellboundaries in one or more of the immunofluorescence images. The outputof the neural network may identify the boundary of a cell in anysuitable way, for example, by identifying pixels showing the cellboundary and/or by identifying pixels at least partially (e.g.,partially or fully) within the cell boundary. In order to identify cellboundaries, the neural network model may be applied to one or moreimmunofluorescence images that were generated using membrane markers. Insome embodiments, the fourth trained neural network is implemented usinga U-Net architecture or a region-based convolutional neural networkarchitecture. In some embodiments, the fourth trained neural networkcomprises at least one million parameters.

In some embodiments, the fourth trained neural network (e.g., CNN) mayhave been trained using a set of training immunofluorescence images oftissue samples as input images and associated output images comprisinginformation indicative of locations of cells in the input images. Anysuitable training technique for training neural networks may be applied,as aspects of the technology described herein are not limited in thisrespect.

It should be appreciated that the embodiments described herein may beimplemented in any of numerous ways. Examples of specificimplementations are provided below for illustrative purposes only. Itshould be appreciated that these embodiments and thefeatures/capabilities provided may be used individually, all together,or in any combination of two or more, as aspects of the technologydescribed herein are not limited in this respect. For example, variousfigures describe various processes and sub-processes that can beperformed according to the techniques described herein, which can beused both individually and/or in combination with each other.

Each of the multiple processes and sub-processes can be performed on thesame MxIF image(s) and/or using one or multiple types of informationdetermined based on the MxIF image(s). Characteristics described hereincan be obtained from the same set of MxIF image(s) and/or the sametissue sample.

The techniques developed by the inventors constitute a substantialimprovement to conventional techniques for processing MxIF images. Asdescribed herein, some of the techniques involve using machine learningmethods to perform one or more of: identifying cell types of tissuecells, determining cell segmentation data (e.g., data indicative of thelocations of cells in the tissue sample, the size and/or shape of thecells, a cell segmentation mask, etc.), checking for tissue damageduring the MxIF staining process (e.g., by analyzing images taken duringdifferent staining steps), identifying communities of cells (e.g.,groups of cells that represent different structures in the tissuesample, and may be of different cell types), and/or removing noise fromMxIF images for MxIF image processing (e.g., removing background noise).

The use of these machine learning methods provides for a fully automatedMxIF image processing pipeline that was not otherwise possible withconventional techniques. As described herein, the machine learningmodels include trained neural networks to perform such methods. Theneural networks are trained with large training data sets with specificinput data and associated output data that the inventors discovered andappreciated provide for training machine learning techniques to performthe methods described herein. For example, the inventors discovered thata neural network can be trained to take as input (a) an image withnuclei information (e.g., a DAPI image), (b) an immunofluorescence image(e.g., of cell membranes, cytoplasm, etc.), and (c) information aboutthe location of the cell in the images, and to provide as output thelikelihood that the cell is expressed by markers of the input image(s).The likelihood(s) can be used to generate a marker expression signaturefor the cell under analysis, which in turn can be compared to celltyping data to predict the cell type. As another example, the inventorsdiscovered and appreciated that a graph neural network can be trained totake as input a graph with nodes that represent the tissue cells and tooutput higher dimensional data that can be used to group cells intocommunities. The graph can include, for each node, data for theassociated tissue cell, such as which group of the multiple groups thecell belongs to, cell location information, and average edge length foredges of the node in the graph. The graph neural network can embed thenode data into a higher dimensional space that can be used to clustercells into cell communities. As a result, the trained neural network canidentify tissue structures that are not otherwise identifiable usingconventional techniques. These and other examples are described furtherherein.

The neural networks are trained using such training data to determinethe ultimate parameters of the neural network that allow the trainedneural network to perform its associated function. Such machine learningmodels have a massive number of parameters, such as hundreds ofthousands of parameters, millions of parameters, tens of millions ofparameters, and/or hundreds of millions of parameters as describedherein. As a result, and as described further herein, the trained neuralnetworks can perform tasks in an automated and repeatable manner, andwith high accuracy (e.g., since the trained neural networks are trainedusing training data that results in the models being sufficiently robustso as not to be affected by imaging noise and/or signal leveldistribution differences in the imaging data).

FIGS. 1 and 2 are overviews of the techniques described herein. FIG. 1is a diagram pictorially illustrating an exemplary system 100 for MxIFimage processing, according to some embodiments of the technologydescribed herein. A microscope 110 and computing device 112 are used toobtain a set of one or more MxIF images 102 of a tissue sample, which inthis example include the fluorescent markers for anti-human CD31antibody, CD8 T cells, CD68 antibodies, and NaKATPase. In someembodiments, the different markers can be shown in different colors inthe MxIF Images 102. For example, CD31 antibodies can be shown in red,CD8 T cells can be shown in green, CD68 antibodies can be shown inmagenta, and NaKATPase can be shown in gray. The computing device 112transmits the MxIF images 102 over network 114 to computing device 116.

The tissue sample can be any biological sample obtained from a subject,including but not limited to blood, one or more fluids, one or morecells, one or more tissues, one or more organs (e.g., which can includemultiple tissues), and/or any other biological sample from a subject.The tissue sample can include cellular material (e.g., one or more cellsof one or more cell types) and/or extracellular material (e.g., anextracellular matrix connecting the cells together, cell-free DNA in anextracellular component of the tissue, etc.). As described herein, thetissue sample can be analyzed in vitro at the tissue level and/or at thecellular level.

It should be appreciated that an MxIF immunofluorescence image describedherein can include one or more immunofluorescence images of a sametissue sample. For example, an MxIF image can be a single, multi-channelimage of the same tissue sample (e.g., where each channel is associatedwith a different marker). As another example, an MxIF image may includemultiple images (e.g., each of which may have one or multiple channels)of the same tissue sample. Referring to the one or more MxIF images 102,for example, in some embodiments MxIF image 102 is a single image withmultiple channels for each of the CD31, CD8, CD68, and NaKATPase markers(e.g., such that MxIF image 102 is a four channel image). As anotherexample, MxIF images 102 can include a separate immunofluorescenceimages for each marker, such that there are four one-channel images. Asa further example, MxIF images 102 can include one or moreimmunofluorescence images with multiple channels (e.g., two MxIF imagesthat each have two channels, and/or the like).

While not shown in FIG. 1, various other types of markers can be used,including any marker known in the art to identify cellular structures orcompartments (such as, e.g., membrane markers, cytoplasm markers,nuclear markers, etc.). A marker may be a gene (e.g., DNA or RNAencoding a protein) or a protein. Markers may be intracellular markers(e.g., intracellular proteins), membrane markers (e.g., membraneproteins), extracellular makers (e.g., extracellular matrix proteins),or a combination of two or more thereof. Other examples of markersinclude markers for PCK26 antibodies, DAPI (for DNA), Carbonic anhydraseIX (CAIX), S6, CD3, and/or the like. Further examples of markers includethe following genes (and proteins encoded by such genes): ALK, BAP1,BCL2, BCL6, CAIX, CCASP3, CD10, CD106, CD11b, CD11c, CD138, CD14, CD16,CD163, CD1, CD1c, CD19, CD2, CD20, CD206, CD209, CD21, CD23, CD25, CD27,CD3, CD3D, CD31, CD33, CD34, CD35, CD38, CD39, CD4, CD43, CD44, CD45,CD49a, CD5, CD56, CD57, CD66b, CD68, CD69, CD7, CD8, CD8A, CD94, CDK1,CDX2, Clec9a, Chromogranin, Collagen IV, CK7, CK20, CXCL13, DC-SIGN,Desmin, EGFR, ER, ERKP, Fibronectin, FOXP3, GATA3, GRB, GranzymeB,H3K36TM, HER2, HLA-DR, ICOS, IFNg, IgD, IgM, IRF4, Ki67, KIR, Lumican,Lyve-1, Mammaglobin, MHCI, p53, NaKATPase, PanCK, PAX8, CNAP, PBRM1,PD1, PDL1, Perlecan, PR, PTEN, RUNX3, S6, S6P, SMA, SMAa, SPARC, STAT3P,TGFb, Va7.2, Vimentin, and/or other marker(s). Markers can be detectedusing labeled antibodies or other labeled binding agents that canselectively bind to a marker of interest. The labeled binding agents(e.g., labeled antibodies) can be labeled with luminescent (e.g.,fluorescent), chemical, enzymatic, or other labels. As a result, thetissue images described herein can be obtained using signals fromvarious types of markers, including fluorescent, chemical and/orenzymatic markers. It should therefore be appreciated that in someembodiments the images described herein (e.g., the MxIF images) caninclude information from non-fluorescent markers as well as fluorescentmarkers. However, in some embodiments, the MxIF images only includeinformation from fluorescent signals. In some embodiments, thefluorescent information can include information from fluorescent stains(e.g., DAPI) in addition to information from fluorescent antibodies forspecific markers.

In some embodiments, immunofluorescent signals from tissue or cells areobtained by contacting fluorescently labeled antibodies to tissue orcells (e.g., fixed and/or sectioned tissue or cells) and detectingfluorescent signals (e.g., using fluorescent microscopy) to determinethe presence, location, and/or level of one or more markers of interest.In some embodiments, the fluorescently labeled antibodies are primaryantibodies that bind directly to the markers of interest. In someembodiments, the fluorescently labeled antibodies are secondaryantibodies that bind to unlabeled primary antibodies that bind directlyto the markers of interest. For example, the secondary antibodies maybind to the primary antibodies if the secondary antibodies were raisedagainst antibodies of the host species of the primary antibody.Different techniques can be used for fixing and/or sectioning tissue orcells. For example, tissue or cells can be fixed with formaldehyde orother reagents. In some embodiments, tissue can be fixed by vascularperfusion with a fixative solution. However, tissue or cells also may befixed by immersion in a fixative solution. In some embodiments, fixedtissue or cells can be dehydrated and embedded in material such asparaffin. However, in some embodiments tissue or cells can be frozen topreserve tissue morphology as opposed to being embedded in a materialsuch as paraffin. In some embodiments, tissue or cells (e.g., fixed,embedded, and/or frozen tissue or cells) can be sectioned, for exampleusing a microtome. In some embodiments, sectioned tissue or cells can bemounted on microscope slides or other suitable support for fluorescentmicroscopy. Mounted tissue or cells (e.g., mounted sectioned tissue orcells) can be contacted with one or more primary and/or secondaryantibodies (e.g., involving several incubation, blocking, and/or washingsteps) to obtain labeled tissue or cells.

The computing device 116 processes the MxIF images 102. The computingdevice 116 processes the MxIF images 102 to generate information 104,including information that identifies the locations of cells in thetissue sample (e.g., by segmenting the image of the tissue sample intocells) and the different types of cells in the tissue sample. In someembodiments, the computing device 116 identifies multiple groups ofcells in the tissue sample at least in part by (a) determining featurevalues for at least some of the cells using the MxIF images 102 and theinformation indicative of locations of the at least some of the cells(e.g., determining feature values for at least some of the cellsidentified by the cell location information, such as a cellular mask),and (b) grouping the at least some of the cells into the multiple groupsusing the determined feature values.

The computing device 116 determines one or more characteristics of thetissue sample using the multiple cell groups. In some embodiments, thecomputing device 116 determines information about cell types in thetissue sample. For example, the computing device 116 can determine thecell types of the cells of the multiple cell groups in the tissuesample. Examples of the cell types determined by the computing device116 can include one or more of endothelial cells, epithelial cells,macrophages, T cells (e.g., CD3+ T cells, CD4+ T cells, or CD8+ Tcells), malignant cells, NK cells, B cells, and acini cells. Thecomputing device 116 can determine the cell types based on user inputand/or using artificial intelligence techniques. For example, thecomputing device 116 can process the multiple cell groups using one ormore trained neural networks to determine the cell type(s) in each cellgroup. For example, as described herein cell types can be predictedbased on group information about the cells as well as other relevantinformation (e.g., cell shape/size, neighbors in a graph, etc.) andprocessed by the neural network to determine predicted cell types. Asdescribed further herein, the neural networks can be trained, forexample, using sets of training data, where each set specifies inputdata that includes one or more cell groups and/or other relevant inputdata (e.g., cell shape, masks, etc.) and associated output dataidentifying the cell type(s) of each cell group in the input data. Insome embodiments, the computing device 116 can process the multiple cellgroups based on user input to determine the cell types in each group.For example, the user input can include a manual identification of cellsthat appear related, possible cell types of grouped cells, and/or thelike.

In some embodiments, the computing device 116 can determine a percentageof one or more cell types in the tissue sample. For example, thecomputing device 116 can determine the percentage of one or more ofendothelial cells, epithelial cells, macrophages, T cells, malignantcells, NK cells, B cells, and acini cells in the tissue sample.

The computing device 116 uses the information about the cell locations,cell types, and/or other information (e.g., information regardingphysical parameters of the cells, such as cell area information, densityinformation, etc.) to determine characteristics 106 of the tissuesample, including determining information regarding neighboring cells(e.g., neighboring cell types) and/or the organization of the cells inthe tissue sample. For example, the computing device 116 can determinethe neighboring cell types of cells of a cell type of interest. Suchneighboring cell type information can be indicative of, for example,whether at least some of the cells of the cell type of interest are (a)closely clustered together in one or more clusters (e.g., if the cellsof interest largely neighbor each other), (b) are distributed throughoutthe tissue sample (e.g., if the cells of interest mostly neighbor othertypes of cells in the tissue sample), (c) are grouped together with oneor more other cell types in the tissue sample (e.g., if the cells ofinterest mostly neighbor the one or more other cell types in the tissuesample), and/or other cell neighbor information.

The computing device 116 determines one or more further characteristics108 of the tissue sample based on the MxIF images, information 104(e.g., cell type and/or cell location), and/or information 106 (e.g.,cell neighbors and/or cell organization), such as statisticalinformation (e.g., cell distribution information), spatial information(e.g., distances between cell types), morphological information, and/orthe like. For example, the statistical information can includeinformation about a distribution of at least some cells of the multiplecell types. The distribution information can include, for example,distributions between different cell types (e.g., whether two cell typesare distributed near each other, are mixed with each other in one ormore areas, are separated by a distance, and/or the like), distributionsof one or more cell types within the tissue sample (e.g., informationabout one or more areas in the tissue sample of high or lowconcentration of the cells), and/or other distribution information.

As another example, in some embodiments the spatial information caninclude information about locations of at least some cells of themultiple groups of cells. For example, the spatial information caninclude information about a spatial organization of one or more celltypes of the tissue sample (e.g., including information about cellorganization in the tissue sample by cell type, compared to other celltypes, and/or the like). As another example, the spatial information caninclude information about one or more areas of the tissue sample thatinclude one or more cell types (e.g., areas that include a number ofcells of one or more cell types above a threshold).

As a further example, in some embodiments the morphological informationincludes information about the form and/or structure of the cells (e.g.,such as the shape, structure, form, and/or size of the cells), the formand/or structure of the tissue sample, and/or the like.

The computing devices 112 and 116 can be any computing device, includinga laptop computer, desktop computer, smartphone, cloud computing device,and/or any other computing device capable of performing the techniquesdescribed herein. The network 114 can be any type of network connection,including a wired and/or wireless network connection, such as a localarea network (LAN), wide area network (WAN), the Internet, and/or thelike. While FIG. 1 shows two computing devices 112 and 116 incommunication over network 114, it should be appreciated that otherconfigurations can be used, such as configurations with one computingdevice or more than two computing devices, configurations without anetwork (e.g., if using just one computing device) or with more than onenetwork, and/or other configurations.

FIG. 2 is a diagram 200 showing exemplary MxIF images and related datathat can be generated by processing the MxIF images, according to someembodiments of the technology described herein. The MxIF images 202 inthis example can include various markers such has those for PCK26antibodies, CD8 T cells, CD31 antibodies, CD68 antibodies, DAPI (forDNA), and/or the like described herein. The MxIF images 202 can beprocessed (e.g., using AI-based techniques, as described herein) todetermined various information about the tissue sample. The informationcan include segmentation information 204, which identifies the locationsof the cells in the tissue sample. The information can include one ormore masks 206 that can be applied to an image to identify features ofthe tissue, such as stromal masks that include information regarding thelocations of stroma in the tissue sample and/or tumor masks that includeinformation regarding tumor cells in the tissue sample. The informationcan also include information 208 about cell positions and/or informationon cell neighbors. The information can further include information 210about cell types and an analysis of sub-population(s) of cells. Suchinformation can be used, as shown at 212, to process raw MxIF images 202to identify cellular and/or histological aspects of the tissue sample,including the endothelial cells, macrophages, T cells, and malignantcells in the tissue sample.

FIG. 3 is a diagram 300 showing exemplary components of a pipeline forprocessing MxIF images of a tissue sample to determine characteristicsof cells in the tissue sample, according to some embodiments of thetechnology described herein. The MxIF images are shown as images 310A,310B, 310C, through 310N, referred to generally herein as MxIF images310. As explained above, each MxIF image can be an image of thefluorescence of one or more markers applied to the tissue sample. Thepipeline includes an MxIF image preprocessing component 320 thatperforms one or more preprocessing steps on the MxIF images. Thepreprocessing can include one or more of background subtraction (e.g.,subtracting background noise from the MxIF images), changing the imageresolution, down sampling, resizing, filtering, and/or other types ofimage preprocessing. The pipeline also includes a cell segmentationcomponent 330 that generates information about the locations of cells inthe immunofluorescence images (e.g., by segmenting the image to identifycell locations, such as cell segmentation masks).

The pipeline also includes a cell typing component 340 that performscell typing (e.g., determining cell types) and/or groups cells in thetissue sample into a plurality of different groups based on the celllocation information from the cell segmentation component 330 and thepreprocessed MxIF images from the MxIF image preprocessing component320. The pipeline also includes a cell morphology assessment component350 that uses data from the cell segmentation component 330. The cellmorphology assessment component 350 determines parameters of the cells,such as the cell area, cell perimeter, cell size, and/or the like. Thepipeline also includes a characteristic determination component 360,which uses data from both the cell typing component 340 and the cellmorphology assessment component 350. The characteristic determinationcomponent 360 can determine one or more characteristics of the tissuesample, such as information regarding the distribution of cells,distances between cells, and other information as described herein. Insome embodiments, the distance information may be the length of ashortest path between two cells that does not cross through another cellor structure (or portions thereof). For example, the distanceinformation can be determined based on a closest distance along aportion of the tissue sample. For example, if two cells are separatedthrough acini, the distance may be a measure of the distance around theacini (rather than a distance through the acini).

FIG. 4A is a diagram showing exemplary processing flow 400 of the MxIFimages 300 using some of the components of FIG. 3, according to someembodiments of the technology described herein. As shown on the left ofFIG. 4A, the MxIF images 310 are preprocessed using the MxIF imagepreprocessing component 320. In this example, the MxIF imagepreprocessing component 320 generates a processed MxIF image for each ofthe MxIF images 310, shown as processed MxIF images 410A, 410B, 410C,through 410N, respectively (which are collectively referred to herein asprocessed MxIF images 410). As described herein, for example, theprocessed MxIF images 410 can be processed, such as performingbackground subtraction (e.g., to remove noise). The processed MxIFimages 410 are then provided to and/or otherwise made available to thecell typing component 340. While FIG. 4A shows each of the MxIF images310 being processed by the MxIF image preprocessing component 320, thisis for exemplary purposes only and is not intended to be limiting, asonly one or more of the MxIF images 310 may be processed by the MxIFimage pre-processing component 320.

In some embodiments, the computing device uses the separate processedMxIF images 410 to generate a combined multi-channel processed MxIFimage that is used by the cell clustering component 116 (e.g., whereeach channel is associated with a different marker). For example, asexplained above each marker image can be processed to independentlyperform background subtraction for each marker in said image. Forexample, each marker can be processed to subtract noise associated withthat marker (e.g., since the noise may be different for each marker).The computing device can generate the combined image so that the celltyping component 340 can perform the cell clustering using a singleimage that includes a channel for each of the processed markers.Additionally, or alternatively, the cell typing component 340 canperform cell clustering using a plurality of images.

As also shown in FIG. 4A, the cell segmentation component 330 uses atleast some of the MxIF images 310, shown as MxIF images 310A and 310C inthis example, to generate location information 420 that is indicative oflocations of cells in the tissue sample captured by theimmunofluorescence images. The MxIF images 310 can also be preprocessedusing MxIF image preprocessing component 320 (to generate processed MxIFimages that are used by the cell segmentation component 330, which arenot shown in FIG. 4A). The location information 420 can include, forexample, cell segmentation data, such as cell segmentation masks. Thelocation information 420 is provided to and/or otherwise made availableto the cell typing component 340.

It should be appreciated that FIG. 4A is intended just to show anexemplary processing flow. One or more components shown in FIG. 4A couldbe optional (e.g., MxIF Image Preprocessing 320, cell segmentation 330,etc.) For example, while MxIF Image Preprocessing 320 is shown on boththe left and right of FIG. 4A, it need not be used in either or bothlocations. Further, while FIG. 4A shows the cell segmentation component330 using two MxIF images, this is for exemplary purposes only, as thecell segmentation component 330 can use any number of the MxIF images310, including all of the MxIF images and/or the same number of MxIFimages processed by the MxIF image preprocessing component 320.

Each of the MxIF images 300 can be captured using different markers(e.g., by staining the tissue with different antibody markers), suchthat each immunofluorescence image is captured when the tissue sample issubject to a different marker. As a result, the MxIF staining processcan be cyclic and include staining the tissue sample for imaging,washing out the marker in preparation for a subsequent staining, and soon. The inventors have appreciated that each time the marker is washedout of the tissue sample, local tissue damage can occur. For example,some staining methods, such as the Cyclic Immunofluorescence (CyCIF)staining method, can be disruptive and cause some local tissue damage(e.g., due to the use of hydrogen peroxide). As a result, individualcells and/or groups of cells could be damaged, such as being washed outand/or shifted relative to the original location in the sample. Suchdamage can result in incorrect information for the damaged cells beingprocessed through the image processing pipeline, beginning at the stepat which the damage occurred and cascading through subsequent steps(e.g., and potentially compounded due to further damaged cell(s)). As aresult, in some embodiments the techniques can include checking a tissuefor degradation at one or more steps of the MxIF imaging process.

FIG. 4B is a diagram showing the exemplary processing flow 400 of FIG.4A that also includes a tissue degradation check component 430,according to some embodiments of the technology described herein. Toavoid potential tissue damage from affecting the tissue analysis, thetissue degradation check component 430 can detect damaged cell(s) andexclude those cells from the tissue analysis. In some embodiments,cellular marker(s) of the immunofluorescence images from differentstages can be compared to detect changes in the tissue structure (e.g.,by comparing cell nuclei, cell boundaries, etc. over time for changes).For example, immunofluorescence images of nuclei markers (e.g., DAPI)from different stages can be compared to monitor for changes of cellnuclei locations in the tissue sample over time.

In some embodiments, the tissue degradation check component 430 cancompare markers from different immunofluorescence imaging stages using atrained neural network. The neural network model can be implementedbased on, for example, a ResNets model, such as that described inKaiming He et al., “Deep Residual Learning for Image Recognition,”arXiv:1512.03385v1 (Dec., 2015), available at arxiv.org/abs/1512.03385,which is hereby incorporated by reference herein in its entirety. Such aResNets model implementation can include various numbers of parameters.Such a model can include many parameters, such as at least half amillion parameters, one million parameters, or more. In someembodiments, such a model can include tens of millions of parameters(e.g., ten million parameters, twenty-five million parameters, fiftymillion parameters, or more). For example, the number of parameters canrange from 11 to 55 million parameters based on the implementation. Insome embodiments, the parameters can include at least a hundred millionparameters (e.g., at least one hundred million parameters, between onemillion to one hundred million parameters), hundreds of millionparameters, at least a billion parameters, and/or any suitable number orrange of parameters. As another example, the neural network model can beimplemented based on, for example, an EfficientNet model, such as thatdescribed in Mingxing Tan and Quoc Le, “EfficientNet: Rethinking ModelScaling for Convolutional Neural Networks,” arXiv:1905.11946v5(September, 2020), available at arxiv.org/abs/1905.11946, which ishereby incorporated by reference herein in its entirety. Such anEfficientNet model implementation can also include various numbers ofparameters, such as half a million parameters, at least one millionparameters, multiple millions of parameters (e.g., five millionparameters), tens of millions of parameters (e.g., ten millionparameters, twenty-five million parameters, fifty million parameters, ormore). For example, the number of parameters can range from 5 to 60million parameters. In some embodiments, the parameters can include atleast a hundred million parameters (e.g., at least one hundred millionparameters, between one million to one hundred million parameters),hundreds of million parameters, at least a billion parameters, and/orany suitable number or range of parameters.

In some embodiments, the trained neural network can take as input a setof immunofluorescence marker images (e.g., DAPI marker images) taken ofthe same tissue sample over time. The set of immunofluorescence markerimages can be stained over multiple immunofluorescence imaging loops,such as each of two different loops (e.g., base and test loops). In someembodiments, the trained neural network can take as input a portion ofthe immunofluorescence marker images. For example, in some embodiments,the set of immunofluorescence marker images can be processed using asliding window across the immunofluorescence images to process theimages in smaller portions. The window can be of a certain width (e.g.,128 pixels, 256 pixels, etc.), height (e.g., 128 pixels, 256 pixels,etc.) and number of channels that represents the number of markers(e.g., 2, 3, 4, etc.). The sliding window can move across theimmunofluorescence images in a preconfigured pattern to process the fullcontent of the immunofluorescence images. For example, the slidingwindow can begin at the upper-left corner of the immunofluorescenceimages at a first line, move horizontally across the immunofluorescenceimages until reaching the right side of the immunofluorescence images,move down to a second line of the immunofluorescence images and workagain from left-to-right, and so on, until reaching the bottom-right ofthe immunofluorescence images. The trained neural network can thereforebe used as a convolutional filter for full image processing. The inputmarker images (or portions thereof) can be normalized, such as by usingz-normalization and/or any other normalization technique as appropriate.

In some embodiments, the output of the neural network can be at least avalue indicating whether the window is associated with damaged tissue(e.g., a binary value and/or a probability of whether the window isassociated with damaged tissue). In some embodiments, the output caninclude a value for each of a plurality of classes, with at least one ofthe plurality of classes associated with areas showing signs of tissuedamage. For example, if there are three classes (e.g., OK, EMPTY, andDAMAGED areas of the tissue sample), the output can be three values thateach reflect a probability for a corresponding class of each of thewindows of the immunofluorescence images. In some embodiments, for eachwindow, the class with the highest probability is selected for thatwindow to choose an ultimate class or categorization for the window(e.g., damaged tissue or not). The final output of the tissuedegradation check process can combine the determined class for eachwindow and generate a patch mask that reflects the various classes ofrelevance as described further in conjunction with FIG. 4C. As a result,in some embodiments the patch mask indicates whether tissue is damagedor not down to a granularity provided by the window size.

In some embodiments, an annotated dataset (e.g., annotated by one ormore pathologists) can be used to train the neural network to classifyportions of the tissue samples into a set of classes. In one example, adataset used to train the neural network included 2500 annotated DAPImarker images (although it should be appreciated that any marker canpotentially be used, as long as the same marker is used on multipleloops or steps of the imaging process). The images can be annotatedusing a set of classes that can be used to train the neural network toclassify new data into the classes (e.g., by determining a likelihood ofwhether the new data corresponds to each of the classes). As describedherein, in some embodiments the neural network can be trained toclassify new data into, for example, a first class for empty portions ofthe tissue (e.g., portions without cells), a second class for unchangedportions, a third class for damaged portions, a fourth class forportions to watch over time, and/or the like. The images can beannotated using the windowing approach discussed herein, such that theimages include annotations for windowed portions across the tissuesample on a line-by-line basis (e.g., essentially such that the imagesare divided into a grid and each block of the grid is annotated with aclass). For annotating the immunofluorescence image dataset, in oneexample the UniversalDataTool (UDT) was used, and the windowedsub-portions of each image were classified into three classes (OK,EMPTY, DAMAGED). In some embodiments, the input images can be augmentedusing one or more transformations, such as affine transformations and/orother transformations as appropriate. The training process can be asupervised learning training process.

In some embodiments, the tissue degradation check component 430 cangenerate a patch mask of the tissue sample that represents damagedand/or undamaged portions of the tissue sample. For example, the patchmask can include portions (e.g., corresponding to the windowed portionsprocessed by the neural network) that indicate the different classesthat the portions of the tissue sample were classified into by theneural network. The patch mask can be used to filter out some and/or allof the classified portions to prevent further analysis of damagedportions of the tissue sample. For example, the patch mask can be usedto filter out segmentation contours (e.g., generated by the cellsegmentation module 330) from regions classified as having damage.

FIG. 4C is a diagram showing a patch mask 444 generated based on tissuedegradation information determined by comparing two nucleus markerimages 440A and 440B (collectively stains 440) of a same tissue sample,according to some embodiments of the technology described herein. Thepatch mask 444 can be generated using a tissue degradation checkprocess, such as the processes executed by the tissue degradation checkcomponent 430 in FIG. 4B. In this example, each of the two nucleusmarker images 440 are generated by imaging the tissue sample using DAPImarkers during different steps or cycles of the MxIF imaging process.The window 442A in marker image 440A and the window 442B in marker image440B depicts the window that moves across the marker images 440 toiteratively process the marker images 440 by comparing the windowedportions of the marker images 440 to check for tissue disturbancesacross MxIF imaging steps. The windowed portions of the marker images440 were compared using a trained neural network, which classifiedpatches of the tissue sample into three classes (empty, damaged, andundamaged) as described herein, and the class with the highestprobability is selected for each window. The resulting patch mask 444includes sections 444A to indicate the windowed portions of the tissuesample that were classified as mostly or entirely empty, sections 444Bfor the windowed portions of the tissue sample that include sufficientlyundamaged tissue such that those portions of the tissue sample are OK touse for further analysis, and sections 444C that include a sufficientnumber of damaged cells (e.g., one or more damaged cells, a number ofdamaged cells above a predetermined threshold, etc.) such that thesections 444C should be excluded from further analysis.

FIG. 4D is a diagram showing use of the patch mask 444 from FIG. 4C tofilter areas of a tissue sample for processing, according to someembodiments. The patch mask 444 can be used to filter out the sections444C that included damaged cells. As shown in FIG. 4C, the patch mask444 is applied to the segmentation mask 446 to filter out the sections444C. The result is filtered segmentation mask 448 that can be used inthe image processing pipeline (instead of segmentation mask 446),including for cell typing and further tissue analysis as describedherein. By using the filtered segmentation mask 448, the imageprocessing pipeline (e.g., the cell typing component 340, cellmorphology assessment component 350, and/or characteristic determinationcomponent 360 of FIG. 3) does not process the sections 444C that areremoved from the mask for cell typing and/or characteristicdetermination.

It should be appreciated that while the tissue degradation check module430 is illustrated in FIG. 4B after the cell segmentation module 330,this is for exemplary purposes only. For example, the tissue degradationcheck can be performed as part of the cell segmentation module 330,prior to cell segmentation (e.g., as part of the MxIF imagepreprocessing module 320), and/or at any other point of the process. Insome embodiments, the tissue degradation check can be performed afterbackground subtraction is performed on the MxIF images. Further, itshould be appreciated that the tissue degradation check module 430 isoptional and therefore a tissue degradation check need not be performedas part of the image processing pipeline.

The cell typing component 340 uses the processed MxIF images 410 and thelocation information 420 to perform cell typing and/or to group thecells into a plurality of groups (e.g., based on the cells exhibitingsimilar feature values). The plurality of groups can be used by thecharacteristic determination component 360 to determine one or morecharacteristics of the tissue sample. FIG. 5A is a flow chart showing anexemplary computerized process 500 for processing MxIF images of atissue sample based on cell location data to group cells of the tissuesample to determine at least one characteristic of the tissue, accordingto some embodiments of the technology described herein. The computerizedprocess 500 can be executed by, for example, the computing device 116described in FIG. 1. The computing device 5100 of FIG. 51 can beconfigured to execute one or more of the aspects of the pipelinedescribed in conjunction with FIGS. 3-4B for processing MxIF images.

At step 502, the computing device obtains at least one multiplexedimmunofluorescence image of a same tissue sample (e.g., MxIF images 310discussed in conjunction with FIG. 3). As described herein, the at lastone multiplexed immunofluorescence image can be a single multi-channelimmunofluorescence image of the same tissue sample and/or multipleimmunofluorescence images of the same tissue sample (each of which mayhave one or multiple channels). In some embodiments, the computingdevice obtains the multiplexed immunofluorescence image(s) by obtainingpreviously-generated image(s) (e.g., accessing one or more images storedusing one or more computer-readable storage devices and/or receiving oneor images over at least one communication network). For example, theMxIF images may be captured by imaging the tissue sample using amicroscope, stored for subsequent access prior to execution of process500, and accessed during act 502. In other embodiments, act 502 mayencompass capturing the images. The tissue sample is an in vitro samplethat has been previously obtained from a patient (e.g., from a patienthaving, suspected of having, or at risk of having cancer).

The inventors have appreciated that it can be desirable to process theMxIF images, such as to remove noise from the MxIF images for some ofthe processing described herein. For example, the system may be able toperform cell segmentation (e.g., cell segmentation 330 in FIG. 3) onMxIF images without removing noise, since the noise may not affect thesystem's ability to determine the locations of cells in the tissuesample. However, noise may cause issues with cell clustering (e.g.,performed as part of cell typing 340 in FIG. 3). For example, a lowsignal to noise level can impact cell clustering, and therefore the rawMxIF images and/or the data generated by the cell segmentation processmay not be sufficient for cell clustering.

In some embodiments, the techniques include processing at least one ofthe immunofluorescence images to generate a corresponding processedimage. Processing an immunofluorescence image can include performingbackground subtraction. The background subtraction can, for example,remove at least some noise. The noise can include, for example, noise inthe image caused by the microscope that captured the image, noise causedby aspects of the tissue, such as noise due to surrounding cells (e.g.,fluorescence noise), noise due to antibodies, and/or the like. In someembodiments, the images can be processed at different regions in theimage. For example, noise can be removed from particular regions in theimage since some regions may exhibit noise more than other regions.Additionally or alternatively, in some embodiments the images can beprocessed on a per-image basis and/or a per-channel basis. For example,noise can also be removed on a per-marker basis, since each markerchannel may exhibit different noise.

In some embodiments, the techniques can include applying a trainedneural network model to each of the immunofluorescence images to removeat least some noise. FIG. 17 is a diagram of a convolutional networkarchitecture 1700 for implementing a trained neural network that canpredict a noise (e.g., for background subtraction) subtraction thresholdfor subtracting noise from a raw immunofluorescence image, according tosome embodiments of the technology described herein. The convolutionalneural network architecture 1700 can be implemented and/or executed aspart of the MxIF image preprocessing, such as part of MxIF imagepreprocessing 120 in FIG. 1 and/or MxIF image preprocessing 320 shown inFIGS. 3-4B. The convolutional neural network architecture 1700 can beimplemented and/or performed as part of other processes andsub-processes described herein. For example, the architecture 1700 canbe used to remove noise as part of steps 502, 504 and/or 506 in FIG. 5A.As another example, the architecture 1700 can be used to remove noise aspart of steps 612, 614 and/or 616 in FIG. 6A. As a further example, atrained neural network implemented using the architecture 1700 can beused to remove noise as part of steps 704, 706 and/or step 708 of FIG.7A.

A neural network implemented according to the convolutional networkarchitecture 1700 is trained to predict the threshold images 1706 basedon raw input images 1708. As shown in the example of FIG. 17, theconvolutional network architecture 1700 may have a “U” structure withconvolutional layers 1702A, 1702B and 17002C being first applied to asequence of successively lower-resolution versions of the raw image 1708data (along the down-sampling path) and, second, to a sequence ofsuccessively higher-resolution versions of the raw image 1708 data(along the up-sampling path), shown as up-sampling layers 1704A, 1704Band 1704C. In some embodiments, while not shown in the convolutionalnetwork architecture 1700, the resolution of the data may be decreased(e.g., along the down-sampling path) using one or more pooling layersand increased (e.g., along the up-sampling path) using one or morecorresponding unpooling layers. Such a neural network implementation caninclude various numbers of parameters. Such a model can include manyparameters, such as at least half a million parameters, one millionparameters, two million parameters, five million parameters, or more. Insome embodiments, such a model can include tens of millions ofparameters. For example, the number of parameters can include at leastten million parameters, twenty million parameters, twenty-five millionparameters, fifty million parameters and so on, based on theimplementation. In some embodiments, the parameters can include at leasta hundred million parameters (e.g., at least one hundred millionparameters, between one million to one hundred million parameters),hundreds of million parameters, at least a billion parameters, and/orany suitable number or range of parameters.

In some embodiments, the model can be pre-trained using noise removalinformation. In some embodiments, the model can be trained to usethresholding to remove noise. In such embodiments, the model can betrained using thresholded data, such as thresholded imagesrepresentative of an appropriate threshold for noise removal. Thetraining data can also include immunofluorescence images as the inputimages and corresponding thresholded images (with noise removed) as theoutput images so that the model can learn to generate the thresholdedimages from the raw images. For example, referring further to FIG. 17,the training data can include raw images 1708 and correspondingthresholded images 1706, which when compared have the thresholddifferences 1710. In some embodiments, the thresholding can be performedglobally across the immunofluorescence images (e.g., after a smoothingstep, such as a Gaussian smoothing step). In some embodiments, thetechniques can divide immunofluorescence images into sub-images fornoise removal. For example, the intensity of a marker may change acrossan immunofluorescence image, and therefore the threshold used for oneportion of an image may be different than that used for another portionof an image. The techniques can include, for example, breaking animmunofluorescence image into a set of sub-images of a same size, suchas 256×256, 512×512, 256×512, and/or the like.

Referring further to FIG. 5A, at step 504 the computing device obtainsinformation indicative of locations of cells in the multiplexedimmunofluorescence image (e.g., the cell location information 420discussed in conjunction with FIGS. 4A-4B). The information indicativeof locations of cells can include data indicative of the locations ofsome and/or all of the cells in the immunofluorescence image(s). In someembodiments, the cell location information can include cell boundaryinformation, information from which cell boundary information can bederived (e.g., expression levels of one or more markers of theimmunofluorescence image(s) that are expressed by, for example, cellularmembranes and/or nuclei), and/or masks. In some embodiments, the celllocations for some and/or all of the cells may be the same in each ofthe immunofluorescence images. For example, if the immunofluorescenceimages are all of the same tissue sample, then the cell locations may bethe same in each image. In some embodiments, the cell locationinformation can be specified using a cell segmentation mask, which canbe applied to one or more of the immunofluorescence images to identifythe locations of cells in the immunofluorescence images.

In some embodiments, a mask can be a binary mask and/or multi-valuedmask. A binary mask can, for example, indicate a binary value (e.g.,present or not present) for one or more cells, tissue structure, and/orpixels in the imaged tissue sample. A multi-valued mask can, forexample, indicate a range of values for the pixels, cells, and/or tissuestructure in the imaged tissue sample. For example, a multi-value maskcan be used to also indicate partial presence of a tissue component(e.g., in addition to fully present or not present components), multipleaspects of the tissue (e.g., the presence of different cells, tissuestructure, etc.), and/or other non-binary information. In someembodiments, a cell boundary mask can be created based on cell boundaryinformation obtained from the immunofluorescence images. In someembodiments, a binary cell boundary mask indicates either the presence(e.g., via a white pixel) or absence (e.g., via a black pixel or othernon-white colored pixel) of detected cell boundaries in the tissuesample.

In some embodiments, the computing device accesses the cell locationinformation. For example, the cell location information can be generatedby a separate computing device and transmitted to the computing device(e.g., over wired and/or wireless network communication link(s)), storedin a memory accessible to the computing device, and/or the like.

In some embodiments, the cell location information can be generatedmanually and accessed for use by the techniques described herein. FIG. 8shows an exemplary MxIF image 800 and manual cell location/segmentationdata 802 for the MxIF image, according to some embodiments of thetechnology described herein.

In some embodiments, the computing device generates the cell locationinformation using at least one multiplexed immunofluorescence image. Forexample, referring to FIGS. 3-4B, the computing device can be configuredto execute one or more aspects of the cell segmentation component 330 togenerate the cell location information. In some embodiments, thecomputing device selects a set of channels of the one or moremultiplexed immunofluorescence images for generating the cell locationinformation, where each selected channel includes marker data that isindicative of cell structure information. For example, the selectedchannels can include data of cell membranes, cell types, cell nuclei,and/or the like, which can be used to generate the cell locationinformation. For example, cell nuclei can be shown using DAPI, H3K36TM,and/or other markers. As another example, cell surface markers caninclude CD20, CD3, CD4, CD8, and/or the like. As a further example,cytoplasm markers can include S6 and/or the like. In some embodiments, aplurality of markers are used for generating cell location information.For example, the immunofluorescence markers used to generate the celllocation information can include using DAPI as a cell nuclei marker,NaKATPase as a cell membrane marker, and S6 as a cell cytoplasm marker.

In some embodiments, the computing device uses machine learningtechniques to generate the cell location information. For example, thecomputing device can apply a convolutional neural network model to oneor more of the immunofluorescence image channels to generate celllocation data. In some embodiments, the computing device selects thechannel(s) with cell structure information, and applies theconvolutional neural network model to the selected subset of theimmunofluorescence images to generate the cell location information.

In some embodiments, the convolutional neural network model can includea neural network model with a “U” shape as described in conjunction withFIG. 17, such as a U-Net architecture.

In some embodiments, the neural network architecture can include aregion-based convolutional neural network (R-CNN) architecture, such asa Mask R-CNN. An example of a Mask R-CNN is that described in Kaiming Heet al., “Mask R-CNN,” arXiv:1703.06870 (January, 2018), available atarxiv.org/abs/1703.06870, which is hereby incorporated by referenceherein in its entirety. Such a model can include a large number ofparameters. For example, such a model can include at least half amillion parameters, at least a million of parameters, multiple millionsof parameters (e.g., at least one million parameters, two millionparameters, three million parameters, etc.), and/or tens of millions ofparameters (e.g., at least ten million parameters, twenty-five millionparameters, fifteen million parameters, etc.). For example, such a modelcan include forty (40) to forty-five (45) million parameters (e.g.,forty million parameters, forty-two million parameters, and/orforty-five million parameters). In some embodiments, the parameters caninclude at least a hundred million parameters (e.g., at least onehundred million parameters, between one million to one hundred millionparameters), hundreds of million parameters, at least a billionparameters, and/or any suitable number or range of parameters. The inputto R-CNN model can include multiple channels, including a nuclei markerand one or more membrane markers. For example, the input can includethree channels, including a nuclei marker for the first channel (e.g.,DAPI), a membrane marker that is present on most cells for the secondchannel (e.g., CD45 or NaKATPase), and an additional membrane markerwith sufficient staining quality for the third channel (e.g., includingCD3, CD20, CD19, CD163, CD11c, CD11b, CD56, CD138, etc.). In someembodiments, the input channels can be normalized (e.g., to be withinthe range of 0-1).

In some embodiments, the input image (or images) is split intointersecting squares (e.g., of size 128×128 pixels, 256×256 pixels,etc.) and processed on a window-by-window basis. In such embodiments,the network output can be an array of mask proposals for each separatecell. In some embodiments, the output can be a binary mask for eachwindow. Additionally, or alternatively, the output can be a set ofimages with values that represent the probability of a given pixel beingpart of the cell's mask (e.g., a value in the range of 0-1). For suchembodiments, the pixels of the output images can be thresholded and/orselected among using various techniques to determine the ultimate maskvalues. For example, if each pixel includes two probabilities that addup to 1, then the output pixels can be thresholded using a value of 0.5to obtain the ultimate pixels for a binary mask.

In some embodiments, windows of prediction may include overlapping data.For example, windows may share data and/or intersect if a cell islocated in-between window edges. Such redundancy can be avoided byprocessing only cells from some, but not all, portions of the windows.For example, the pixels of the center, top and right corners of eachimage can only be processed for each window (e.g., such that the downand right parts of the image are processed only if the current window isthe last window from the right or down side). The resulting cellsegments can then be aggregated into the final output mask (e.g., withinteger values representing individual cell instances).

In some embodiments, the architecture (e.g., U-Net, R-CNN, etc.) can usean encoder head (e.g., as the first layer or layers of the model), suchas a ResNets model as described herein (e.g., ResNet-50). As a result,multiple segmentation networks can be created with a similar and/or thesame encoder head to allow for model interchangeability.

In some embodiments, the convolutional neural network model is trainedusing a set of training immunofluorescence images as input images andassociated cell segmentation images as output images. Various trainingset sizes can be used for training a neural network model, such asapproximately 100 images, approximately 200 images, approximately 300images, approximately 2,000 images, 3,000 images, 4,000 images, 5,000images, 6,000 images, 10,000 images, and/or the like. In someembodiments, the training set size can depend on the input image size,such that the training set size may range from approximately 4,000 to5,000 images. In some embodiments, squares of images can be (e.g.,randomly) sampled from original full images (e.g., and therefore fewertraining images may be required). The training immunofluorescence imagescan be of certain tissue samples, and the cell segmentation imagesassociated with the training immunofluorescence images can includelocation information for the cells in the tissue samples. In someembodiments, the neural network model can be trained using multi-channelimages and/or single channel images as the input images.

The training images may include a plurality of markers, one marker foreach channel. For example, a three-channel image (or images) can be usedas described herein, where the first channel is a nuclei marker, thesecond channel is a membrane marker expressed by most cells for tissuetype(s) of interest, and the third channel is an additional membranemarker. For example, three-channel images can be created using DAPI,NaKATPase, and S6 markers. The corresponding output images used fortraining can be manually-generated sample output images (e.g., celllocation information, such as cell segmentation masks with outlines ofcell contours). In some embodiments, the training set (e.g., includingthe input images and/or associated output images) can be preprocessedfor training. For example, a preprocessing step can be performed on thetraining output images to detect bordering cells (e.g., which caninclude cells with a number of intersecting pixels greater than athreshold, such as greater than 3 pixels of the cell borders). FIG. 9shows examples of images that can be used to train a neural network toidentify cell location information, according to some embodiments of thetechnology described herein. Each set of images 902, 904, 906 and 908shows the composite immunofluorescence image, the corresponding neuralnetwork prediction image of the cell locations, the training image ofthe cell locations used to train the neural network, and an image of thecell boundary contacts (e.g., where the cells contact other cells).

The neural network model can be trained using various backpropagationalgorithms. In some embodiments, the convolutional neural network istrained using a backpropagation algorithm with ADAM optimizer with 0.001learning rate. At each training step, the neural network model istrained with multi-channel input images to produce cell locationinformation. For example, in some embodiments the neural network modelcan be trained using three-channel input images to produce two-channeloutput images (e.g., one channel with a segmentation map and anotherchannel with a boundary cell contact map) that closely match theassociated training output images. The neural network model can betrained using various loss functions. For example, a categoricalcross-entropy loss function can be used since the model is performing apixel classification task.

FIG. 10 is a diagram pictorially illustrating an example of using atrained convolutional neural network model 1000 to process MxIF imagesobtained of a tumor to generate cell segmentation data 1002, accordingto some embodiments of the technology described herein. In the exampleof FIG. 10, the MxIF image 1010 includes a first marker image 1012 and asecond marker image 1014. The computing device uses the trained neuralnetwork model 1000 to process the first marker image 1012 and the secondmarker image 1014 to generate the cell location/segmentation information1002.

As described in conjunction with FIG. 17, the convolutional neuralnetwork model has a “U” structure in which convolutional layers areapplied to successively lower-resolution versions of the data along thedown-sampling path 1000A and, then, to successively higher-resolutionversions of the data along the up-sampling path 1000B. As indicated bythe red arrows, the resolution of the data may be decreased along thedown-sampling path 1000A using one or more pooling layers. As indicatedby the green arrows, the resolution of the data may be increased alongthe up-sampling path 1000B using one or more corresponding unpoolinglayers.

FIG. 11 is a diagram pictorially illustrating another exemplary use of atrained neural network 1100 to process immunofluorescence images togenerate cell location/segmentation data 1102, according to someembodiments of the technology described herein. In this example, theMxIF image 1110 includes DAPI marker image 1112, and NaKATPase markerimage 1114, where DAPI is a fluorescent DNA stain and NaKATPase is amembrane marker. It should be appreciated that DAPI, NaKATPase, and/orother markers can be used. For example, other markers can include acytoplasm marker S6, a membrane marker PCK26, Carbonic anhydrase IX(CAIX), CD3, and/or the like. The computing device uses the trainedneural network 1100 to process the DAPI marker image 1112 and theNaKATPase marker image 1114 to generate the cell location/segmentationinformation 1102.

FIGS. 12-16 show examples of immunofluorescence images and associatedcell location information (e.g., in this example, cell segmentationmasks), according to some embodiments of the technology describedherein. The cell location data can be accessed by the system and/orgenerated by the system, such as by the cell segmentation module 330 ofFIG. 3. FIG. 12 shows a MxIF image 1200 and cell segmentation data 1202generated based on the MxIF image 1200, according to some embodiments ofthe technology described herein. FIG. 13 shows a composite fluorescenceimage 1300 and cell segmentation data 1302 generated based on thecomposite fluorescence image, according to some embodiments of thetechnology described herein. FIG. 14 is a diagram showing exemplary cellsegmentation data for exemplary MxIF images obtained of kidney tissue,according to some embodiments of the technology described herein. Theexemplary cell segmentation data 1402 was determined based on MxIFimages 1404 and 1406, and exemplary cell segmentation data 1408 wasdetermined based on MxIF images 1410 and 1412. Images 1404 and 1410include DAPI, CAIX, PCK26, and ki67 protein markers. Images 1406 and1412 include CD31, CD8, CD68 and NaKATPase markers.

FIG. 15 shows a clear cell renal cell carcinoma (CCRCC) MxIF image 1500and corresponding cell segmentation data 1502, according to someembodiments of the technology described herein. FIG. 16 shows a CCRCCMxIF image 1600 and corresponding cell segmentation data 1602, accordingto some embodiments of the technology described herein.

Referring further to FIG. 5A, the computing device identifies multiplegroups of cells in the tissue sample at least in part by performingsteps 506-508. At step 506, the computing device determines featurevalues for at least some of the cells using the immunofluorescenceimages and the information indicative of locations of the at least someof the cells. In some embodiments, the techniques can includedetermining feature values for most of the cells in theimmunofluorescence images and/or all of the cells in theimmunofluorescence images.

In some embodiments, the techniques can include determining featurevalues for cells based on at least one pixel value associated with alocation of the cell in at least one of the immunofluorescence images.As described further herein, the feature values can include and/or bedetermined based on values of pixels in the multiplexedimmunofluorescence image for the location of the at least some of thecells (e.g., pixels at or near the location of the cells). For example,if the multiplexed immunofluorescence image includes multiple singlechannel images, the techniques can use pixel values of respectivelocations of the cells in the single channel images (e.g., which may bethe same location across the different images, and/or different images,depending on how the images were acquired). As another example, if theimages include one or more multi-channel images, the location in themulti-channel image may be the same location for each channel. In someembodiments, the feature values include one or more values derived fromvalues of pixels in the cells. For example, the feature values caninclude a contribution determined for each of the channel(s) of theimmunofluorescence image(s). A contribution can be, for example, a valueindicative of how much the pixels associated with a cell contribute(e.g., are expressed) for that channel. For example, a contribution canbe determined for each channel that ranges from 0% (e.g., nocontribution) to 100% (e.g., full contribution). The contribution can bedetermined according to various techniques that summarize the pixels ofthe cells, such as by determining an average across pixels at the celllocation, a mean across pixels at the cell location, a proportion ofpositive pixels in the cell to negative pixels in the cell, and/or thelike.

In some embodiments, the techniques can determine feature values for thecells using pixel values of immunofluorescence at or near the locationsof the cells in the immunofluorescence images. FIG. 5B shows an exampleof feature values 600 for an associated cell location of cell locationdata 602, according to some embodiments of the technology describedherein. The feature values can be determined by, for example, the celltyping component 340 of FIG. 3. In the example of FIG. 5B, the featurevalues 600 include a percentage for each channel 1, channel 2 throughchannel N, where N is a whole number that represents the number ofchannels associated with the cell location. Each channel can beassociated with, for example, one or more immunofluorescence markers,one or more immunofluorescence images, and/or the like. The featurevalues 600 show that channel 1 has a 25% contribution (e.g., a meancontribution) towards the cell location, channel 2 has a 40%contribution towards the cell location, and channel N has an 85%contribution towards the cell location.

Referring further to FIG. 5A, at step 508 the computing device groupsthe cells into the multiple groups using the determined feature values.FIG. 7A is a flow chart showing a computerized process 700 forprocessing MxIF images of a tissue sample based on cell location data tocluster the cells of the tissue sample into multiple cell groups,according to some embodiments of the technology described herein. Thecomputerized process 700 can be performed by, for example, the computingdevice 116 described in conjunction with FIG. 1. For example, thecomputing device 5100 of FIG. 51 can be configured to execute one ormore aspects discussed in conjunction with the cell typing component 340of FIGS. 3-4B. The computerized process 700 can be performed as part ofother processes and sub-processes described herein. For example, thecomputerized process 700 can be performed as part of step 508 of FIG. 5Ato group the cells into multiple cell groups.

At step 702, the computing device selects a cell to compute one or morefeature values for the cell. At step 704, the computing device selectsan immunofluorescence image. At step 706, the computing deviceidentifies a set of pixels at or near the location of the selected cellin the immunofluorescence image. For example, the techniques can includeidentifying a set of pixels that are at least partially (e.g., partiallyor fully) within a cell boundary of the cell.

At step 708, the computing device determines a feature value for thecell based on the determined set of pixels. In some embodiments, thetechniques can include determining the feature value based on the pixelvalues of the set of pixels. The feature value may include a summarizedvalue based on the pixel values of the set of pixels, such as an averagevalue across the set of pixels or an indication of the proportion ofpixels that satisfy one or more criteria (e.g., presence of a respectivemarker, fluorescence value associated with a respective marker above athreshold, etc.). In some embodiments, the feature value can berepresentative of a contribution of each of one or moreimmunofluorescence markers and/or one or more immunofluorescence imagesto the cell, as described herein. In some embodiments, the computingdevice can determine the feature value based on how the pixel valuescontribute to the cell. For example, the computing device can determinethat the pixel values of the determined set of pixels includefluorescence present and/or present above a certain threshold for acertain percentage of the cell location (e.g., 30% of the cell location,40% of the cell location, etc.).

At step 710, the computing device determines whether to analyzeadditional immunofluorescence images for the selected cell. For example,the computing device can be configured to determine a feature value fora certain number of immunofluorescence markers (e.g., e.g., one marker,two markers, all of the markers, etc.), for a certain number ofimmunofluorescence images (e.g., one image, two images, all of theimages, etc., which may have one or multiple channels) and/or the like.As another example, the computing device can be configured to determinea feature value for each of a certain set of immunofluorescence markersand/or images associated with certain features (e.g., immunofluorescencemarkers and/or images associated with certain markers indicative of cellstructure, etc.). If the computing device determines at step 710 that itis to analyze one or more additional immunofluorescence images, thecomputing device proceeds back to step 704 and selects anotherimmunofluorescence image.

If the computing device determines at step 710 that there are no furtherimmunofluorescence images to analyze for the selected cell, thecomputing device proceeds to step 712 and determines whether there areone or more cell locations to determine feature value(s) for. Forexample, the computing device may be configured to determine featurevalue(s) for a certain set of cells (e.g., a certain number of cells,cells in one or more locations, and/or the like) and/or for all of thecells. If the computing device determines at step 712 that it is todetermine feature value(s) for one or more further cells, the computingdevice proceeds back to step 702 and selects another cell.

If the computing device determines that there are no further cells toanalyze, the computing device proceeds to step 714 and performs cellclustering (e.g., as part of cell typing 340) to group the cells intoone or more cell groups based on the determined feature value(s). Insome embodiments, the techniques include analyzing the feature value(s)to determine relationships among cells (e.g., similar markerexpressions, marker expressions that match known cell typing data, aprobability analysis of cells being of a same type and/or having similarproperties, etc., as described herein), and grouping cells based on thedetermined relationships such that each cell in a cell group has featurevalues that are indicative of a relationship among the cells in the cellgroup.

The techniques can include applying one or more clustering algorithms(e.g., unsupervised clustering algorithms) to identify relationshipsamong cells based on the determined feature values. Some examples ofclustering algorithms that can be used to group cells includehierarchical clustering, density-based clustering, k-means clustering,and/or any other suitable unsupervised clustering algorithm, aself-organizing map clustering algorithm, a minimum spanning treeclustering algorithm, and/or the like, as aspects of the technologydescribed herein are not limited in that respect. In some embodiments,the techniques can perform cell clustering using the FlowSOM algorithm,which can analyze the data using a self-organizing map.

At step 510, the computing device determines at least one characteristicof the tissue sample using the multiple cell groups. In someembodiments, the techniques can include generating a report indicativeof the at least one characteristic. The report can also include otherinformation, such as information about the plurality of groups and/orany other information determined about the at least one MxIF image asdescribed herein. The report can be provided to a user. For example, thereport can be provided by displaying the report via a graphical userinterface (e.g., via a web-based or in an application program executingon a device, transmitting an electronic file (e.g., a PDF file or a filein any suitable format) to the user, and/or any other sufficienttechnique to provide the report to the user.

In some embodiments, the techniques include using the immunofluorescenceimages obtained at step 502 in conjunction with the cell groupinginformation obtained at step 504 to determine the one or morecharacteristics of the cells of the tissue sample. The at least onecharacteristic of the tissue sample may characterize the cellularcomposition of the tissue sample (e.g. cell type, cell morphology, etc.)and/or organization of the tissue sample (e.g. cell localization,multi-cellular structure localization, etc.). As described herein, theone or more characteristics can include, for example, cell types in thetissue sample (e.g., where each group is associated with a differentcell type). In some embodiments, the computing device identifies celltypes of individual cells in the tissue sample. In some embodiments, thecomputing device identifies cell types of at least a thresholdpercentage of the individual cells in the tissue sample. For example,the threshold percentage can be at least 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, etc.

As another example, the one or more characteristics can includestatistical information about a distribution of the cells, such as adistribution of one or more cell types in the tissue sample (e.g.,distributions among the cells of a group), distributions betweendifferent cell types (e.g., distributions between different groups ofcells), and/or the like. As another example, the computing device candetermine spatial information about locations of the cells, such as aspatial organization of one or more cell types (e.g., where each celltype is associated with a different group) of the tissue sample, such ascell neighbor information indicative of which type(s) of cells neighborother type(s) of cells and/or cell contact information indicative ofwhat type(s) of cells contact other type(s) of cells. As a furtherexample, the computing device can determine morphological informationabout the size, shape, and structure of the cells and/or other aspectsof the tissue, such as cell areas, cell perimeters, cell sizes, and/orthe like. It should be appreciated that any of the characteristicsdescribed herein can be determined for a tissue sample. Therefore, insome embodiments the techniques include determining a plurality ofcharacteristics for the tissue sample, such as cell type, celldistribution information, spatial information, morphologicalinformation, multi-cellular structure organization, and/or any othercharacteristics described herein.

In some embodiments, the techniques include performing cell typing bydetermining relationships among the cells based on the feature values,such as by using a probabilistic analysis based on the feature values.As discussed in conjunction with FIGS. 5A-5B, channel contributions(e.g., mean channel contributions) can be used to determine cell types,which can be used to determine the cell groups. However, in somesituations the inventors have appreciated that such channelcontributions may not provide for stable cell typing. For example, theinventors have appreciated that the channel contributions can depend onthe quality of segmentation, the size of the cells, the shape of thecells, the conditions of the tissue staining, and/or the like. Asanother example, the inventors have appreciated that channelcontributions do not take into account information about intracellularsignal localization (e.g., which can be useful to help distinguish realsignal data from noise). As a further example, the inventors have alsoappreciated that without an ability to set the range of values ofchannel contributions for specific types of cells, it can be difficultto purposefully search for cell types of interest (e.g., instead,clustering can be used, which may not find cells of interest). As anadditional example, given such potential problems, results may need tobe checked manually, which can increase delays and impact automation.

To address such potential problems, the inventors have developedtechniques for cell typing that leverage a marker expression signature.FIG. 6A is a flow chart showing an exemplary computerized process 610for processing MxIF images of a tissue sample based on cell locationdata of a cell in the tissue sample to predict a type of the cell,according to some embodiments of the technology described herein. Thecomputerized process 610 can be performed by, for example, by the celltyping component 340 of FIG. 3. The computerized process 610 can beperformed as part of other processes and sub-processes described herein.The computerized process 610 can be performed, for example, as part ofsteps 506 and/or 508 of FIG. 5A.

At step 612, the computing device obtains at least one multiplexedimmunofluorescence image of a tissue sample that was obtained using MxIFimaging (e.g., as described in conjunction with step 502 of FIG. 5A). Asdescribed herein, the marker images can be provided as separate imagesand/or as combined images with multiple channels. The at least oneimmunofluorescence image can include various markers, such as of nucleimarker(s) (e.g., FOXP3, DAPI), membrane marker(s) (e.g., CD3e),cytoplasm marker(s) (e.g., CD68), and/or the like. At step 614, thecomputing device obtains information indicative of a location of a cellin the at least one multiplexed immunofluorescence image. For example,the location information can include cell boundary information,information from which cell boundaries can be determined (e.g., markerexpressions), and/or one or more cell masks (e.g., a cell mask for oneor more of the received immunofluorescence images) as described inconjunction with step 504 of FIG. 5A.

FIG. 6B shows examples of immunofluorescence images and cell locationdata that can be used for cell typing, according to some embodiments.FIG. 6B shows data for a first set of data for a first cell 622A and asecond set of data for a second cell 622B (collectively cells 622). Foreach cell 622, the data includes a nuclei marker (DAPI) image 624, acell segmentation (binary) mask 626, and a set of marker images 628 thatcan be used to search for various expressions of the cell. In thisexample, the marker images 628 can be roughly categorized asintranuclear marker images 628A (FOXP3 images), membrane marker images628B (CD3e and CD68 images), and cytoplasmic images 628C (CD68) based onthe marker image localization.

At step 616, the computing device determines a marker expressionsignature that includes, for each particular type of marker of the atleast one multiplexed immunofluorescence image obtained at step 612, arespective likelihood that the particular type of marker is expressed inthe first cell. Therefore, the cell location information (e.g., cellmasks) can be used to identify cell locations in the immunofluorescenceimages. In some embodiments, the computing device can use a trainedneural network to determine the marker expression signature. The neuralnetwork can be trained to determine the likelihood of whether (or not) amarker signal is present and expressed for the cell. The neural networkmodel can be implemented based on, for example, a ResNets model asdescribed herein. As described herein, such a model can include a largenumber of parameters. For example, such a model can include at leasthalf a million parameters, at least a million of parameters, multiplemillions of parameters (e.g., at least one million parameters, twomillion parameters, three million parameters, etc.), and/or tens ofmillions of parameters (e.g., at least ten million parameters, fifteenmillion parameters, twenty-five million parameters, fifty millionparameters, etc.). For example, such a model can include forty (40) toforty-five (45) million parameters (e.g., forty million parameters,forty-two million parameters, and/or forty-five million parameters). Insome embodiments, the parameters can include at least a hundred millionparameters (e.g., at least one hundred million parameters, between onemillion to one hundred million parameters), hundreds of millionparameters, at least a billion parameters, and/or any suitable number orrange of parameters.

The input to the model can be at least one multiplexedimmunofluorescence image of different markers, such as separateone-channel MxIF images, a two-channel MxIF image, a three-channel MxIFimage, etc. Each immunofluorescence image can have a height (e.g., 128pixels, 256 pixels, etc.) and a width (e.g., 128 pixels, 256 pixels,etc.). In some embodiments, the first channel or image can be, forexample, a DAPI image (e.g., cropped to be centered by cell ofinterest), the second channel or image can be another immunofluorescencemarker image (e.g., also cropped), and the third channel or image can bea segmentation mask (a segmentation mask of only one cell of interest).Referring to FIG. 6B, for example, for each cell 622, the input to themodel can include a nuclei marker image 624, a segmentation mask 626,and one of the marker images 628 (e.g., one of the intranuclear images628A, membrane images 628B and cytoplasmic images 628C). In someembodiments, the input images (e.g., the MxIF images or channels) can benormalized. For example, the pixel values of the images can benormalized to be within a range of 0-1, which can be performed based onthe bit depth of the image. For example, the values of an 8-bit imagecan be divided by 255, the values of a 16-bit image can be dived by65535, and/or the like. Such normalization can be used in conjunctionwith other processes and sub-processes described herein, such as part ofthe image preprocessing that is performed by the MxIF imagepreprocessing module 320 discussed in conjunction with FIGS. 3-4B.

The neural network can output either a binary classification (e.g., a 0or 1 for the classification) and/or the likelihood of the cell having asignal of proper intensity and shape (e.g., in the 0 to 1 range) for theassociated input marker image. For example, referring to FIG. 6B, forthe data set 622A, for the input of the nuclei marker image 624, thecell segmentation mask 626, and the intranuclear image 628A (FOXP3), theoutput can be the likelihood of the intranuclear image 628A having aproper intensity and shape of a cell that would be expressed by theintranuclear image 628A.

The neural network can be trained to process the at least onemultiplexed immunofluorescence image (or a portions thereof) and thelocation information. For example, the neural network can be configuredto compare a portion of the at least one multiplexed immunofluorescenceimage (e.g., 128×128 pixels) that includes the cell in the center of theimage as identified by a cell mask. As described herein, theimmunofluorescence image(s) can be nuclei marker images of the area aswell as other expression images, including cell contour images ormembrane images, intranuclear images, cytoplasmic images, and/or thelike. A sample data set with such information can be used to train theneural network to distinguish whether a marker image is likely a realmarker expression for the cell (or not) based on the intensity and shapeof the marker image.

The neural network can be trained on a library of images. As an example,the neural network can be trained on immunofluorescence images and/orcell location data that is annotated by professional pathologists. In anillustrative example not intended to be limiting, three differenttraining sets can be used: a first set with 2,186 images (training set)and 403 images (test set) of 4 markers for nuclei localized markers(e.g. Ki67); a second set with 9,485 images (training set) and 1,979images (e.g., validation set) of 28 markers for membrane localizedmarkers (e.g. CD3, CD20); and a third set with 898 images (training set)and 427 images (validation set) of 8 markers with defined localization(e.g. CD68). As a result, the neural network can be trained to providethe likelihood of whether the marker image(s) of a cell are a realmarker expression.

In some embodiments, the techniques can include executing a plurality oftrained neural networks to determine the marker expression signature.For example, different neural networks can be trained and used fordifferent markers. In some embodiments, different neural networks can beused for markers with different cell localization information (e.g.,nuclei, membrane or cytoplasmic markers). For example, a first neuralnetwork can be trained to detect expression of intranuclear images, asecond neural network can be trained to detect expression of cellmembrane images, a third model can be trained to detect expression ofcytoplasmic images, and/or the like.

In some embodiments, the marker expression signature for the tissuesample can include a set of probabilities or likelihoods indicative ofwhether a cell is expressed for each marker of the at least oneimmunofluorescence image. In some embodiments, the likelihood values canrange from 0 (e.g., no expression) to 1 (e.g., expression). Eachlikelihood can be determined based on not only the marker intensity inthe image, but also using other information that can be determined basedon the cell mask (e.g., the form of the cell, pixel intensity across thecell area, etc.).

At step 618, the computing device compares the marker expressionsignature to cell typing data. In some embodiments, the cell typing datacan include, for each cell type, a set of known marker signature entriesfor a set of markers (e.g., the set of markers of the at least onemultiplexed immunofluorescence image). Each known marker signature caninclude binary data (e.g., 0 or 1) indicative of whether the marker isan expression or not for the cell (e.g., where 0 means no expression and1 means that expression should be seen in the associated cell type). Insome embodiments, if a particular marker is ambiguous and/oruninformative, and therefore is not necessarily an expression or not(e.g., the marker may be expressed or not expressed for a cell), thedata for the marker can indicate as such (e.g., by including both 0 and1). As an example of cell typing data, a cell typing table can begenerated based on known marker expressions of cells as provided inliterature and/or databases, such as the Cluster of Differentiationavailable from Sino Biological (e.g.,www.sinobiological.com/areas/immunology/cluster-of-differentiation)and/or human immune cell markers available from Bio-Rad (e.g.,www.bio-rad-antibodies.com/human-immune-cell-markers-selection-tool.html).

In some embodiments, the marker expression signature can be compared tothe cell typing data to generate comparison data that compares themarker expression signature to known expression signatures of the cellsin the cell typing data. For example, the comparison data can indicate acomparison between each probability of the marker expression signatureand the associated marker values of each known marker signature forcells in the cell typing data. The comparison data can include acomparison metric, such as a distance between the expression signatures,a percentage of overlap of the expression signatures, a similarity scorebetween the expression signatures, and/or any other applicablecomparison that can be used to compare the marker expression signaturewith the known cell signatures in the cell typing data. For example, adistance between the marker expression signature and known markerexpression signatures of the cell typing data can be determined by usinga cosine distance, a Euclidian distance, a Manhattan distance, and/orthe like. At step 620, the computing device determines, based on thecomparison, a predicted cell type. For example, referring to FIG. 6B,the first set of data 622A can be processed to predict a RegulatoryT-cell type, and the second set of data 622B can be processed to predicta Macrophage CD68+ cell type.

In some embodiments, the computing device can analyze the computedcomparison of each cell type of the cell typing data to select a topcandidate cell type. For example, when computing a distance, thecomputing device can select among the various cell types by choosing thecell type with the smallest distance. In order to attempt to only haveone top candidate, the cell typing data can be configured to includeunique entries for each cell type (e.g., such that the comparison doneat step 618 does not result in the same value for multiple cell types inthe cell typing data).

FIG. 6C shows an example of using a neural network 634 to generatemarker expression signature 636 that is compared to cell typing data 638to determine a predicted cell type 640, according to some embodiments ofthe technology described herein. In some embodiments, the markerexpression signature can be a list, array, table, database, etc., or anydata structure as appropriate, of probabilities for each marker. Thecell typing data can likewise include a list, array, table, or otherdata structure of the binary value(s) for each marker. FIG. 6D shows anexample of using a neural network 650 to generate a set of probabilities(shown in this example as a probability table 652) that is compared tocell typing data (shown in this example as cell typing table 654, whichincludes a known expression signature for each cell type) to determine apredicted cell type, according to some embodiments of the technologydescribed herein. The neural network 650 processes input 656, whichinclude images 656A (DAPI marker), 656B (FOXP3 marker), 656C (CD19marker), 656D (CD11c marker) and 656E (CD3e marker), as well assegmentation mask 656F for the cell. As described above, separate neuralnetworks can be used to process applicable marker images. For thisexample, each neural network execution can include processing (a) one ofthe images 656B (FOXP3 marker), 656C (CD19 marker), 656D (CD11c marker)and 656E (CD3e marker), along with CD4 and HLA-DR images that are notshown, in conjunction with (b) the image 656A (DAPI marker) and (c) thesegmentation mask 656F for the cell.

Therefore, a first trained neural network can be used to process theintranuclear FOXP3 marker image, a second trained neural network can beused to process the membrane marker images (e.g., CD3e image 656E andCD11c image 656D), and so on such that separate neural networks can beused to process images based on different localizations of the markers.Therefore, while only one neural network 650 is shown in FIG. 6D,multiple different trained neural networks can be used to processassociated marker image(s).

In this example of FIG. 6D, the neural network 650 generates thefollowing probabilities in the probability table 652: 0.96 for FOXP3marker, 0.54 for CD3e marker, 0.66 for CD4, 0.003 for CD19, 0.002 forHLA-DR, and 0.0005 for CD (with the probabilities closer to 0 meaningless of a probability of expression, and those closer to 1 meaning ahigher probability of expression for the marker).

The computing device computes a cosine distance between theprobabilities in table 652 and the values for the markers for each celltype. In this example, a “+” means the marker is expressed by the cell,while a “−” means the marker is not expressed by the cell. Thecomparison results in cosine distance values of 0.028 for the T-reg celltype, 0.339 for the CD4 T-cell type, 0.99 for the B-cell type, and 0.99for the Myeloid cell type. In this example, the smaller the distance themore likely that the associated cell type is the cell type of the cellunder analysis. As a result, the computing device selects T-reg (withthe lowest distance value of 0.028) as the cell type for the cell.

FIG. 6E shows an example of using a neural network 660 to generate a setof probabilities (shown in this example as a probability table 662) thatis compared to cell typing data (shown in this example as cell typingtable 664) to determine a predicted cell type, according to someembodiments of the technology described herein. The neural network 660processes input 666, which include MxIF images 666A (DAPI marker), 666B(CD68 marker), 666C (CD19 marker), 666D (CD11c marker) and 666E (CD3emarker), as well as segmentation mask 666F for the cell. As describedherein, one or more neural networks can be used to process each of theimages 666B, 666C, 666D and 666E (in conjunction with the DAPI image666A and the segmentation mask 666F). In this example, the neuralnetwork 660 generates the following probabilities in the probabilitytable 652: 0.63 for CD68 marker, 0.0006 for CD3e marker, 0.01 for CD4,0.001 for CD19, 0.0004 for HLA-DR, and 0.69 for CD11c (again, with theprobabilities closer to 0 meaning less of a probability of expression,and those closer to 1 meaning a higher probability of expression for themarker).

The computing device computes a cosine distance between theprobabilities in table 662 and the values for the markers for each celltype. In this example, a “+” means the marker is expressed for aparticular cell, a “−” means the marker is not expressed for aparticular cell, and a “+−” means the marker may or may not be expressedfor the cell. For this example, the comparison results in cosinedistance values of 0.001 for the Macrophage CD68+ cell type, 0.992 forthe CD4 T-cell type, 0.999 for the B-cell type, and 0.478 for theMyeloid cell type. In this example, the smaller the distance is againthe more likely that the associated cell type is the cell type of thecell under analysis. As a result, the computing device selectsMacrophage CD68+ (with the lowest distance value of 0.001) as the celltype for the cell.

In some embodiments, the techniques can include identifying clusters orcommunities of cells based on cell characteristics. For example, it canbe desirable to search for certain cell structures in a tissue sample,such as a cell structure indicative of a cancer (e.g., breast cancer,renal carcinoma, etc.). The techniques can include identifying cellcommunities in a tissue sample, and identifying information of thosecells, such as cell types, distances (e.g., to provide informationregarding sparsely populated cell clusters, close cell clusters, etc.).The communities of cells can include cells of different types. In someembodiments, the cell clusters represent at least a part of a tissuestructure of the tissue sample. The tissue structure can include, forexample, mantle tissue, stromal tissue, a tumor, a follicle, a bloodvessel, and/or any other tissue structure.

FIG. 7B is a flow chart showing an exemplary computerized process 750for identifying communities by obtaining and processing a first set ofcell features (local cell features), according to some embodiments ofthe technology described herein. In the example of FIG. 7B, thecomputerized process 750 processes the obtained local cell featuresusing a graph neural network 772 to identify one or more communities ofcells 778. The computerized process 750 can be executed by, for example,the cell typing component 340 of FIG. 3. The computerized process 750can be performed as part of other processes and sub-processes describedherein. For example, the computerized process 700 can be performed aspart of step 508 of FIG. 5A to group the cells into multiple cell groupsand/or as part of step 510 to determine at least one characteristic(e.g., communities of cells) for the tissue sample. At step 752, thecomputing device generates local cell features, which includes obtainingcell data 774 (e.g., locations of cells, a cell mask, and/or other celllocalization data) and triangulating the cell data 774 to generate agraph 776 at step 752A. For example, the computing device can beconfigured to use triangulation techniques (e.g., Delaunaytriangulation) to construct an arrangement of the cells 774 (e.g., basedon centroids of the cells) to obtain the graph representation 776 of thecellular structure of the tissue. In some embodiments, the graph 776includes a number of nodes that is equal to the number of detectedcells. In some embodiments, aspects of the graph generation can beconstrained. For example, in some embodiments the edge length of eachedge in the graph 776 can be limited to below an upper limit edgethreshold (e.g., based on a pixel distance, such as 200 pixels, 300pixels, etc.). Therefore, the number of edges in the graph 776 can varydepending on cell distances in the tissue, such that the techniques caninclude pruning edges that are below a predetermined length threshold.

The computing device then computes, based on the graph representation ofthe tissue 776, the local cell features at step 752B. The local cellfeatures can include information about the cells that can be determinedbased on the cell data 774 and/or the graph 776. For example, the localcell features can include, for each cell, a cell type, cell neighborsdetermined based on the edges of the graph 776, neighboring cell types,neighbor distance data (e.g., median distance to neighbors, mask-relateddata (e.g., a percentage of area filled with positive pixels for markermasks under each cell (e.g., a CD31 mask for blood vessels, etc.)),and/or the like. Each node can therefore have an associated set of localdata points (e.g., represented as a vector). In some embodiments, thenode data can include the cell type, which can be encoded using aplurality of variables. For example, if there are seven discovered celltypes in the tissue sample, then “cell type 6” can be encoded as [0, 0,0, 0, 0, 1, 0]. In some embodiments, the node data can include themedian value of lengths of all node edges for the cell. In someembodiments, the node data can include the percentage of positive pixelsof a given mask for a cell, which can be extended to include data foreach of a plurality of masks (if present). In some embodiments, the datacan include the percentage of the cells located within one or more masksof selected markers (e.g., a percentage of the area of the cell maskfilled with positive cells). Such mask-based data can allow thecomputing device to leverage information about cells and/or structuresthat may otherwise be difficult to segment. As a result, in someembodiments the total number of data points for each node is L, which isthe sum of (1) the number of cell types, (2) the number of masks toconsider (if any), and (3) a value for the median distance of edges ofgiven node.

The graph 776 can be encoded for input into the graph neural network772. In some embodiments, the node information can be stored in a matrixwith dimensionality n by L, where n is a number of nodes and L is thenumber of node features. In some embodiments, the graph is encoded, suchas into a sparse adjacency matrix (e.g., with dimensionality n by nnodes), into an adjacency list of edges, and/or the like.

At step 754, the computing device inputs the graph (e.g., an encodedversion of the tissue graph 776) to the graph neural network, whichembeds the local cell features into a higher dimensional space. Thegraph neural network processes the input graph using the structure ofthe graph, including the edges and nodes of the graph. The graph neuralnetwork can have different architectures. In some embodiments, the graphneural network 772 is an unsupervised convolutional graph neuralnetwork. For example, the graph neural network can be implemented usinga Deep Graph Infomax architecture that uses graph convolutional networklayers of any suitable type to perform embedding in graphs. The DeepGraph Infomax architecture is described in, for example, PetarVeličković et al., “Deep Graph Infomax,” ICLR 2019 Conference BlindSubmission (Sep. 27, 2018), available atopenreview.net/forum?id=rklz9iAcKQ, and/or arXiv:1809.10341, which ishereby incorporated by reference herein in its entirety. Examples ofdifferent types of convolution layers that can be used in the graphneural network include GCN as described in Thomas Kipf and Max Welling,“Semi-Supervised Classification with Graph Convolutional Networks,”arXiv1609.02907 (Feb., 2017), available at arxiv.org/abs/1609.02907, orSAGEConv as described in William Hamilton et al., “InductiveRepresentation Learning on Large Graphs,” arXiv1706.02216 (September,2018), available at arxiv.org/abs/1706.02216, which are both herebyincorporated by reference herein in their entirety. In addition to graphconvolutional layers, the graph neural network can have a discriminationlayer that is used in training. The neural network implementation caninclude various numbers of parameters, such as at least half a millionparameters, one million parameters, two million parameters, five millionparameters, or more. In some embodiments, such a model can include tensof millions of parameters (e.g., at least ten million parameters,fifteen million parameters, twenty million parameters, twenty-fivemillion parameters, fifty million parameters, and so on, based on theimplementation). In some embodiments, the parameters can include atleast a hundred million parameters (e.g., at least one hundred millionparameters, between one million to one hundred million parameters),hundreds of million parameters, at least a billion parameters, and/orany suitable number or range of parameters.

The graph neural network can be trained to reconstruct each nodeembedding using a variant of noise contrastive estimation, such that theneural network essentially learns internal representations of nodes(e.g., cells) in order to discern between correct representations (e.g.,based on real data) and incorrect representations (e.g., based on noiserepresentations). The graph neural network can be used for featurerepresentation since the graph neural network can represent both localand global features of the tissue structure from information includingcell labels, neighboring cells, cell distances, mask sizes (e.g., insome radius), and/or other input. The node embeddings generated by thegraph neural network can therefore include not only local node or cellneighborhood information, but also preserve a global context throughglobal features. The resulting feature embeddings can therefore includea predetermined same number of features for each cell (e.g., representedusing a fixed size vector, array, and/or other data structure).

In some embodiments, the output of the neural network can be theactivations of the last graph convolutional layer of the network. Sincedifferent types of convolution layers can be used (e.g., GCN, SAGEConv,etc. as described above), the number of dimensions of the output canvary, such as 16 dimensions, 32 dimensions, etc. Each dimension of theoutput can be an embedding from a space with a higher dimensionality toa lower dimensionality, and can generally be thought of as an aggregateof information from the tissue structure for representation. There cantherefore be at least some correlation between the value of embeddingsand specific cell type compositions, for example. In some embodiments,as described further below, clustering can be performed using theseembeddings and the clusters can be described in terms of prevalent celltypes in them.

In some embodiments, a data set can be generated to train the graphneural network 772 that includes, for each of a plurality of trainingtissue samples, a graph representation of the tissue sample and localcell features computed as described above for steps 752A and 752B (e.g.,for each node, the cell type, mask-related data, and median distance ofedges). In some embodiments, a loss function can be maximized, such asloss=log(P_(i))+log(1−P′_(i)), where P_(i) is the probability that thenode (a cell obtained through cell segmentation) is similar to all graphnodes, and P′_(i) is the probability that a permutated node is similarto all graph nodes. As described herein, each node has associatedinformation (e.g., cell type assignment, cell median distance toneighbors, mask information, etc.), which can be in the form of afeature vector. In some embodiments, the Deep Graph Infomax architectureuses a summary vector, which can be the mean value of feature vectorsfor all nodes in a given sample graph. The classifier layer of thearchitecture can be trained to distinguish using embeddings between (a)a given node feature vector that is classified as belonging to the graphsummary vector, and (b) a permuted (e.g., shuffled in place) nodefeature vector that is classified as not belonging to the graph summaryvector.

At step 756, the cell embeddings (including the neighborhood data) areclustered to determine one or more clusters 778. Various clusteringtechniques can be used to determine the clusters. For example, asdescribed herein the techniques can include using a centroid-basedclustering algorithm (e.g., K-means), a distribution based clusteringalgorithm (e.g., clustering using Gaussian mixture models), adensity-based clustering algorithm (e.g., DBSCAN), a hierarchicalclustering algorithm, PCA, ICA, and/or any other suitable clusteringalgorithm. For each determined cluster, the percentage of cells withinthe cluster and associated mask(s) can be used to generate descriptiondata for each cluster as shown in 778.

FIG. 7C shows examples of an image 780 of tissue contours shaded basedon cell types and an image 782 with the contours shaded based on cellclusters (e.g., determined as discussed in conjunction with FIG. 7B) forthe same tissue sample, according to some embodiments. The image 782demonstrates how cell clusters can be used to more clearly visualizeparts of some tissue structures (e.g., follicles of the dark, lightand/or mantle zones) compared to shading based on cell types in image780.

FIG. 18 is a diagram 1800 illustrating exemplary tissue characteristicsthat can be determined by processing MxIF images, according to someembodiments of the technology described herein. One or more of thetissue characteristics can be determined by, for example, thecharacteristic determination module 360 in FIG. 3. The tissuecharacteristics discussed in conjunction with FIG. 18 can be determinedas part of various processes and sub-processes described herein. Forexample, one or more of the tissue characteristics can be determined aspart of step 510 of FIG. 5A. As shown, the characteristics can includeand/or be used to determine cell spatial co-occurrences 1802, which canbe used for radius checks 1804 and/or triangulation 1806. Thecharacteristics can be used to generate one or more masks 1808. Thecharacteristics can include and/or be used to classify cell groups 1810.In some embodiments, the techniques can include classifying the cellgroups using graph neural networks.

In some embodiments, the cell grouping information can be used todetermine one or more masks that can be applied to theimmunofluorescence images to indicate aspects of the tissue. An exampleof a mask is a tumor mask that includes data indicative of the cells ofa tumor in the tissue sample. Another example of a mask is an acini maskthat includes data indicative of the spacing among the cells in thetissue sample, which can identify ducts that form gaps/spaces betweenthe cells. For example, the acini mask can show the ductal tubes of atumor that produce secretions, and can therefore provide informationregarding the shape of the tubes (e.g., since different tumors may havedifferent shapes/sizes of the tubes). A further example of a mask is astroma mask that includes information indicative of the supportivetissue and/or stromal cells in the tissue sample.

In some embodiments, the masks can be used to identify cells indifferent regions of the tissue sample. For example, masks of the acini,tumor, and stroma can be created and used to understand where certaincells are in the tissue sample, such as T cells. For example, the maskscan be used to identify T cells in stromal areas and/or non-stromalareas.

FIG. 19A is a diagram of a stroma mask 1902 and an acini mask 1904generated by processing an immunofluorescence image 1900, according tosome embodiments of the technology described herein. As described above,the stroma mask 1902 includes data indicative of the supportive tissueof the tissue sample, and the acini mask 1904 includes data indicativeof the spacing of the cells in the tissue sample. In some embodiments,the techniques can include generating a stromal mask using a cytoplasmicmarker (e.g., S6 marker, which is expressed in most, if not all, cells)to identify cytoplasmic areas in the tissue sample and excluding areasidentified using an epithelial marker (e.g., PCK26 epithelial marker).In some embodiments, the cytoplasmic marker image can be smoothed and/orthresholded to perform noise removal as described herein. In someembodiments, the epithelial marker image can have holes filled (e.g.,such that inner portions of contiguous shapes are filled, and thereforeremoved from the ultimate stromal mask). In some embodiments, thetechniques can include generating an acini mask using an epithelialmarker (e.g., a smoothed PCK26 mask), which can be inverted and stromalzone(s) can be removed to generate the ultimate acini mask. Epithelialmarkers can be used to generate other masks as well, such as tumor masks(using a smoothed PCK26 mask).

In some embodiments, objects can be masked based on cell communityinformation (e.g., determined as discussed in conjunction with FIG. 7B).For example, an object mask can be created based on the cell communityinformation, such that each one or more cell communities identified inthe cell community information correspond to an associated object in themask. Objects in the object mask can therefore correspond to singleand/or multicellular structures identified in the cell communityinformation. FIG. 19B is a diagram of using features of a tissue sampleto generate an object mask 1956, according to some embodiments of thetechnology described herein. The object mask 1956 can be generated basedon cell features of the tissue sample, such as cell coordinates, celllabels (e.g., community information), and image shape data, as shown by1950 and 1952. In particular, image 1950 in this example shows cellcontours shaded based on community type, and image 1952 in this exampleshows cells that belong to identified communities. One or more objectdetection algorithms can be used to process the cell features todetermine regions (e.g., using Voronoi tessellation) and to countobjects. For example, the object detection algorithms can generate celllabels and count the cell labels to generate Voronoi regions/cells froma tessellation of the tissue sample (e.g., a randomly createdtessellation of the tissue sample). As a result, a density heatmap 1954can be generated based on the object detection algorithms. The densityheatmap 1954 is shaded with dark or light colors based on the number ofcells detected in each Voronoi cell from image 1952. The computingdevice can perform thresholding to create the object mask 1956 ofobjects with high density of the cells with chosen labels. As anillustrative example, lymph node follicles can be detected based oncommunities relative to dark, light and mantle zones of the lymph nodetissue sample.

FIG. 20 is a diagram 2000 showing examples of measuring acini shape,area and perimeter characteristics, according to some embodiments of thetechnology described herein. The diagram 2000 includes three images2002, 2004 and 2006. The diagram 2000 shows corresponding acini masks2008, 2010 and 2012 for images 2002, 2004 and 2006, respectively. Asshown in FIG. 20, each acini mask 2008, 2010 and 2012 has acorresponding set of parameters 2020, 2022 and 2024, including a measureof the area of the acini structure in the acini mask in pixels, ameasure of the area of the acini structure as a percentage of the acinimask (e.g., the percentage of mask pixels to the total number of imagepixels), and a measure of the perimeter of the acini structure inpixels. Such parameters can provide information about the acinistructure. For example, comparing the acini area to acini perimeter(e.g., as a ratio) can provide information about the overall structureof the acini. The diagram 2000 also shows corresponding fibrosis masks2014, 2016 and 2018 for images 2002, 2004 and 2006, respectively. Thefibrosis masks show connective tissue, which is a stromal mask and cantherefore be generated as described herein for stromal masks. Eachfibrosis mask 2014, 2016 and 2018 has a corresponding set of parameters2026, 2028, and 2030, including a measure of the area of the fibrosismask filled by stromal tissue, and a measure of the variance indistribution of the stromal tissue (e.g., which can provide informationregarding how spread out the stromal tissue is in the mask).

FIG. 21 is a diagram showing examples of spatial distributioncharacteristics, according to some embodiments of the technologydescribed herein. FIG. 21 includes examples 2102 of measuring fibrosisdistributions and examples 2104 of measuring t cell distributions. Thefibrosis heatmaps 2102A and the t cell heatmaps 2104A can be shaded toindicate fibrosis and t cell densities, respectively. For example, adarker color (e.g., dark red) can indicate the most dense areas, alighter color (e.g., light red) can indicate the next most dense areas,then another color (e.g., light blue) can indicate further less denseareas, and another color (e.g., dark blue) can show the least denseareas (e.g., and may be another darker color to improve contrast fromdenser areas). In some embodiments, the distribution can be used toprovide a concentration measure of the stromal tissue. For example, thevariance in distribution of the stromal tissue (e.g., as discussed inconjunction with FIG. 20) can be determined based on the standarddeviation of the stromal distribution. In some embodiments, for example,the stromal distribution can be determined by splitting a stromal maskimage into squares of equal size (e.g., using tessellation), and arepresentative value can be determined for each square for comparisonwith other squares. For example, the percentage of pixels indicative ofstromal tissue can be used as a distribution for which standarddeviation can be calculated. In such examples, the techniques canmeasure how homogenously distributed the stroma is in the mask. Forexample, top fibrosis distribution 2106 has an even distribution that isindicative of the mask not including large areas without stroma, whilebottom fibrosis distribution 2108 is not even and therefore indicativeof the mask including areas with no stroma.

FIG. 22 is a diagram showing examples of spatial organizationcharacteristics, according to some embodiments of the technologydescribed herein. FIG. 22 includes examples 2202 of measuring thedistribution of endothelial cells in positive areas in the tumor mask(in the malignant cells) and in negative areas in the tumor mask (innon-malignant compartments), and examples 2204 of measuring thedistribution of T cells in the malignant cells and non-malignantcompartments.

FIG. 23 is a diagram showing an example of cell contact information forimmunofluorescence images for two different patients, according to someembodiments of the technology described herein. As described herein, thecell contact information can be one of the determined characteristic(s)of the tissue sample. For example, the cell contact information can bedetermined as part of step 510 of FIG. 5A, and can be determined basedon multiple groups of cells determined for a tissue sample (e.g., whereeach group is associated with a different cell type). Immunofluorescenceimage 2302 is of a tissue sample of patient 1, and immunofluorescenceimage 2304 is of a tissue sample of patient 2. Both images 2302 and 2304include markers for CD8, CD68, CD31 and PCK26. The characteristicinformation 2306 includes, for each of patient 1 and 2, information 2308regarding the percentage of T cells in contact with blood vessels,information 2310 regarding the percentage of macrophages in contact withblood vessels, and information 2312 regarding the percentage of T cellsin contact with macrophages.

FIG. 24 is a diagram showing examples of information regarding cellneighbors for the two different patients of FIG. 23, according to someembodiments of the technology described herein. As described herein, thecell neighbor information can be one of the determined characteristic(s)of the tissue sample, and can indicate which types of cell(s) neighborother cell type(s). The cell neighbors can include both cell type(s) incontact with a cell type, as well as cells that are not in contact withthe cell type but also do not have other cell(s) between them. FIG. 24includes the immunofluorescence images 2302 and 2034 from FIG. 23. FIG.24 also includes characteristic information 2306 showing the number ofneighbors of T cells for each of immunofluorescence images 2302 and2034.

FIG. 25 shows two examples of MxIF images 2502, 2504 and correspondingstromal segmentation masks 2506, 2508, respectively, according to someembodiments of the technology described herein.

FIGS. 26-35 show various aspects of cell groups and relatedcharacteristic information determined using the techniques describedherein. FIG. 26 shows exemplary MxIF images 2602 and 2604, segmentationmasks 2606 and 2608, and corresponding cell groups 2610 and 2612,according to some embodiments of the technology described herein.

FIG. 27 shows an example of a full MxIF slide processing to generatecell groups, according to some embodiments of the technology describedherein. FIG. 27 shows a view 2702 of the cell group information and ablown-up view 2704 of portion 2706 of the view 2702. The cell groupsinclude CD3 T cells, CD4 T cells, CD5 T cells, macrophages, bloodvessels, and malignant zone cells.

FIG. 28 is a diagram of a restored cell arrangement 2802 generated byprocessing immunofluorescence image 2804, according to some embodimentsof the technology described herein. The restored cell arrangement 2802shows cells in the tissue sample (e.g., where each of the cell typescorrespond to a different group of cells). The restored cell arrangement2802 shows acini cells, CD4 FOXP3 T cells, CD4 T cells, CD8 T cells,endothelium cells, K167 and PCK26 cells, macrophages, and unclassifiedcells.

FIG. 29 shows a 4′,6-diamidino-2-phenylindole (DAPI) stainedimmunofluorescence image 2902 and two images 2904 and 2096 of differentcell groups for the DAPI image, according to some embodiments of thetechnology described herein. Image 2904 includes markers for CD31, CD21,CD3, and CD68. Image 2906 also includes markers for CD31, CD21, CD3, andCD68, and further includes CD20.

FIG. 30 shows cell groups for different CCRCC tissue samples 3002, 3004and 3006, according to some embodiments of the technology describedherein.

FIG. 31 is a diagram showing exemplary cell groups for the exemplaryMxIF images 1404, 1406, 1410 and 1412 obtained of kidney tissue fromFIG. 14, according to some embodiments of the technology describedherein. Cell groups 3102 were determined based on MxIF images 1404 and1406, and cell groups 3104 were determined based on MxIF images 1410 and1412. The cell groups include T cells, macrophages, blood vessels, andthe malignant zone.

FIG. 32 shows a set of images 3200 of cell groups for different clearcell renal cell carcinoma (CCRCC) tissue samples, according to someembodiments of the technology described herein. The images 3200 showcell groups for T cells, NK cells, B cells, macrophages, blood vessels,and the malignant zone. FIG. 33 shows a series of images 3300 of cellgroups for different CCRCC tissue samples, according to some embodimentsof the technology described herein. Like with FIG. 32, the images 3300show cell groups for T cells, NK cells, B cells, macrophages, bloodvessels, and the malignant zone. The techniques described herein cantherefore allow for identifying, for example, both T cells and themalignant zone in the tissue samples.

FIG. 34 is a diagram showing an analysis of two different MxIF images ofCCRCC tissue samples, according to some embodiments of the technologydescribed herein. FIG. 34 includes CCRCC tissue samples 3402 and 3404and corresponding characteristic information 3406 and 3408,respectively, for fifteen areas 3410 in image 3402 and fifteen areas3412 in in image 3404 (e.g., determined as part of step 510 of FIG. 5A).The characteristic information shows, for each area 3410 and 3412, thepercentage of B cells, CD4 T cells, CD8 T cells, endothelium, epitheliumcells, macrophages, macrophages 206, macrophages 68, NK cells, tumorcells, tumor K167+, and unclassified cells in the area. Thecharacteristic information classifies the fifteen areas 3410 as eitherfibrosis/normal or tumor areas, and classifies the fifteen areas 3412 asfibrosis/tumor, normal, or tumor areas.

FIG. 35 is a diagram showing an analysis of a MxIF image of a CCRCCtissue sample 3502, according to some embodiments of the technologydescribed herein. The analysis information 3504 shows for each of elevenareas 3506 of the tissue sample 3502, the percentage of B cells, CD4 Tcells, CD8 T cells, endothelium, epithelium cells, macrophages,macrophages 206, macrophages 68, NK cells, tumor cells, tumor K167+, andunclassified cells in the area. FIG. 35 also shows a blown-up view 3508of exemplary area 9.

FIGS. 36A-41 generally show examples of tissue cell characteristicsdetermined using the techniques described herein. FIGS. 36A-B arediagrams illustrating cell quantities and proportions, according to someembodiments of the technology described herein. FIG. 36A includescharacteristics 3602 that show for different exemplary patients (e.g.,patient RP8393, etc.) the percentage of unclassified cells, endotheliumcells, macrophages, CD8 T cells, CD4 T cells, tumor K167+ cells, andtumor cells. FIG. 36B includes characteristics 3604 for CD8 T cells,characteristics 3606 for endothelium cells, characteristics 3608 formacrophage cells, characteristics 3610 for tumor cells, andcharacteristics 3612 for tumor cells.

FIG. 37 is a diagram illustrating cell distribution characteristics,according to some embodiments of the technology described herein. FIG.37 shows a tissue image 3702 for a first patient and a correspondingstroma mask 3704, both with T cells shown in red. FIG. 37 also shows atissue image 3706 for a second patient and a corresponding stroma mask3708, both with macrophage cells shown in red. Characteristics 3710 showthat for images 3702 and 3704, just under 90% of the T cells are in thestroma, and the remaining percent are in the tumor. Characteristics 3712show that for images 3706 and 3708, nearly all of the macrophage cellsare in the stroma, with only a small percent in the tumor.

FIGS. 38A-B are diagrams of percentage heatmaps and distributiondensities of histological features, according to some embodiments of thetechnology described herein. The heatmaps can be generated based on thecell groups, such as in conjunction with steps 508 and 510 of FIG. 5A.Each block in the heatmaps represents the percentage of the block thatcorresponds to a positive stroma mask. FIG. 38A includes a stroma mask3802 for a first patient, a corresponding stroma heat map 3804 and agraph of the stroma distribution density 3806. FIG. 38A also includes astroma mask 3808 for a second patient, a corresponding stroma heat map3810 and a graph of the stroma distribution density 3812. FIG. 38Bincludes a stroma mask 3818 of a third patient and corresponding graphsof the x-sum intensity 3814 and y-sum intensity 3816. FIG. 38B alsoincludes a stroma mask 3824 of a fourth patient and corresponding graphsof the x-sum intensity 3820 and y-sum intensity 3822.

FIG. 39 is a diagram showing cell neighbor and cell contactcharacteristics, according to some embodiments of the technologydescribed herein. The cell contact characteristics can be determined,for example, at step 510 of FIG. 5A. FIG. 39 includes a first table 3902for a first patient showing the percentage of neighboring cells for CD8T cells, endothelium cells, macrophage cells, tumor cells, andunclassified cells (where the neighboring cells can similarly be CD8 Tcells, endothelium cells, macrophage cells, tumor cells, andunclassified cells), and an associated circular chart 3904 showing thesame neighboring cell percentages in relation to the various types ofcells. FIG. 39 also includes a second table 3906 for a second patientshowing the percentage of neighboring cells for CD8 T cells, endotheliumcells, macrophage cells, tumor cells, and unclassified cells, and anassociated circular chart 3908 showing the same neighboring cellpercentages in relation to the various types of cells.

FIG. 40 is a diagram showing examples of tSNE plots 4002-4016 ofcharacteristics of marker expression, according to some embodiments ofthe technology described herein. In particular, tSNE plot 4002 is forCD45 marker expression, tSNE plot 4004 is for CAIX marker expression,tSNE plot 4006 is for CD163 marker expression, tSNE plot 4008 is forCD206 marker expression, tSNE plot 4010 is for CD31 marker expression,tSNE plot is for CD3E marker expression, tSNE plot 4014 is for PBRM1marker expression, and tSNE plot 4016 is for PCK26 marker expression.The tSNE plots can be built using marker expressions and used to displaytwo types of information as shown in FIG. 40—intensities of markerexpressions in cells (each point is a cell), and in the graph 4020, thecell types by different colors and/or shadings in order to show acorrespondence between cells with a high marker expression level andtheir assigned cell type. The characteristics can be determined, forexample, at step 510 of FIG. 5A as described herein.

FIG. 41 is another diagram showing examples of tSNE plots 4102-4148 ofcharacteristics of marker expression, according to some embodiments ofthe technology described herein. In particular, tSNE plot 4102 is forCD138 marker expression, tSNE plot 4104 is for IgD marker expression,tSNE plot 4106 is for CD31 marker expression, tSNE plot 4108 is forSPARC marker expression, tSNE plot 4110 is for BCL2 marker expression,tSNE plot 4112 is for CD10 marker expression, tSNE plot 4114 is for CD20marker expression, tSNE plot 4116 is for CD3 marker expression, tSNEplot 4118 is for Collagen marker expression, tSNE plot 4120 is for CD163marker expression, tSNE plot 4122 is for HLA-DR marker expression, tSNEplot 4124 is for CD11c marker expression, tSNE plot 4126 is for CD21marker expression, tSNE plot 4128 is for CD68 marker expression, tSNEplot 4130 is for Ki-67 marker expression, tSNE plot 4132 is for CD25marker expression, tSNE plot 4134 is for FOXP3 marker expression, tSNEplot 4136 is for CD8 marker expression, tSNE plot 4138 is for CD44marker expression, tSNE plot 4140 is for CD35 marker expression, tSNEplot 4142 is for CD4 marker expression, tSNE plot 4144 is for CD45marker expression, tSNE plot 4146 is for BCL6 marker expression, andtSNE plot 4148 is for IRF4 marker expression.

FIGS. 42-47 generally relate to using the techniques described herein inthe context of 4′, 6-diamidino-2-phenylindole (DAPI) univen markerstaining. FIG. 42 is a diagram pictorially illustrating use of aconvolutional neural network 4202 to determine cell segmentation data4204 for a 4′, 6-diamidino-2-phenylindole (DAPI) univen stainedimmunofluorescence image 4206, according to some embodiments of thetechnology described herein.

FIG. 43 is a diagram pictorially illustrating a first cell mask 4302generated based on a combination of a DAPI stained immunofluorescenceimage 4304 and a CD3 cellular marker image 4306 of a tissue sample,according to some embodiments of the technology described herein. Thecell masks can be obtained, for example, as part of the informationindicative of the locations of cells as described in conjunction withstep 504 of FIG. 5A. As shown in the cell mask examples in FIG. 43, thecell mask 4302 can be applied to different immunofluorescence images toconvey the expressions of marker(s) for the cells of the tissue sample.

FIG. 44 is a diagram pictorially illustrating a second cell mask 4402generated based on a combination of the DAPI stained immunofluorescenceimage 4304 of FIG. 43 and a CD21 cellular marker image 4404, accordingto some embodiments of the technology described herein. FIG. 45 is adiagram pictorially illustrating a third cell mask 4502 generated basedon a combination of the DAPI stained immunofluorescence image 4304 ofFIG. 43 and a CD11c cellular marker image 4504, according to someembodiments of the technology described herein.

FIG. 46 is a diagram pictorially illustrating a blood vessel mask 4602generated based on MxIF images 4604 (with image 4606 being an expandedview of a portion of image 4604) of the tissue sample of FIG. 43,according to some embodiments of the technology described herein. Theblood vessel mask 4602 shows blood vessel placement in the tissue. Forthis example, a CD31 marker is shown in a first color shade (e.g., red)on top of a DAPI nuclei marker in a second color shade (e.g., blue) in acomposite pseudocolor picture 4604 to highlight that calculated mask4602 corresponds to blood vessels in the tissue. As described herein,the blood vessel mask can be created as part of, for example, step 510of FIG. 5A.

FIG. 47 is a diagram pictorially illustrating cell groups 4702 generatedusing the masks 4302, 4402, 4502 and 4602 from FIGS. 43-46, according tosome embodiments of the technology described herein. The cell groups4702 include T cells (green), follicular dendritic cells (dark orange),CD11c+ cells (blue), and blood vessels (red).

FIG. 48 shows a set of cell groups 4802 for a prostate tissue sample4804 and a malignant area 4806, according to some embodiments of thetechnology described herein. FIG. 48 encircles an area of a dense tumorthat was mechanically affected during staining process, with most of thetumor still being segmented. FIG. 49 shows an enhanced view of a portion4902 of the cell groups 4802 of the prostate tissue sample 4804 of FIG.48 to highlight immune infiltration, according to some embodiments ofthe technology described herein. FIG. 50 shows a set of cell groups5002, 5004, 5006 and 5008 for prostate tissue samples taken from fourdifferent patients and visualized according to some embodiments of thetechnology described herein. Each cell in FIGS. 48-50 is coloredaccording to its cell type, with masks of blood vessels colored red. Thebinary black and white images in FIGS. 48-49 are full slide segmentationmasks of the fibrosis obtained using a neural network.

An illustrative implementation of a computer system 5100 that may beused in connection with any of the embodiments of the disclosureprovided herein is shown in FIG. 51. For example, the computer system5100 may be used to implement the computing device 112 and/or thecomputing device 116. The computer system 5100 can be used to executeone or more aspects of the MxIF image processing pipeline shown in FIGS.3-4B, 5A, 6A, and/or 7A-7B. The computer system 5100 may include one ormore computer hardware processors 5102 and one or more articles ofmanufacture that comprise non-transitory computer-readable storage media(e.g., memory 5104 and one or more non-volatile storage devices 5106).The processor 5102(s) may control writing data to and reading data fromthe memory 5104 and the non-volatile storage device(s) 5106 in anysuitable manner. To perform any of the functionality described herein,the processor(s) 5102 may execute one or more processor-executableinstructions stored in one or more non-transitory computer-readablestorage media (e.g., the memory 5104), which may serve as non-transitorycomputer-readable storage media storing processor-executableinstructions for execution by the processor(s) 5102.

The computer system 5100 can be any type of computing device with aprocessor 5102, memory 5104, and non-volatile storage device 5106. Forexample, the computer system 5100 can be a server, desktop computer, alaptop, a tablet, or a smartphone. In some embodiments, the computersystem 5100 can be implemented using a plurality of computing devices,such as a cluster of computing devices, virtual computing devices,and/or cloud computing devices.

FIGS. 52-53 provide two comparisons to demonstrate how the techniquesdescribed herein can outperform conventional techniques for both celllocation and cell shape reproduction, according to some embodiments. Theground truth data for both FIGS. 52-53 was a different non-small-celllung cancer tissue sample, and the markers included DAPI, NaKATPase,PCK26, CD3 and CD68 were used to guide manual segmentation bypathologists for the ground truth data. To provide an example ofconventional techniques, the ground truth data was segmented usingCellProfiler 4.1.3, which is available from the Broad Institute atcellprofiler.org/releases (referred to herein for ease of illustrationas “CellProfiler”).

To demonstrate the techniques described herein, cell segmentation wasperformed using a trained convolutional neural network to generate thecell segmentation data (referred to for the examples of FIGS. 52-53 forease of illustration as “Mask R-CNN”). In particular, the trained modelused for this example implemented a Region-Based CNN architecture with aResNet-50 encoder head. The model had approximately forty-three (43)million parameters.

The input to Mask R-CNN is three channels, including a nuclei marker(including DAPI) for the first channel, a membrane marker that ispresent on almost every cell for specific tissue type for the secondmarker (including CD45 or NaKATPase), and the third marker is oneadditional membrane marker with sufficient staining quality (includingCD3, CD20, CD19, CD163, CD11c, CD11b, CD56, CD138, etc.). All channelsare normalized to be within the range of 0-1, and as a combination thevalue of maximum expression in each pixel among all selected markers wasused.

In this implementation, the Mask R-CNN segmentation process includes aforward network pass and a post-processing step to generate the ultimatecell segmentation data. The input image is split into intersectingsquares of size 256×256 pixels. The network output is an array of maskproposals for each separate cell. In this implementation, the outputswere not binary images, rather the output was a set of images withvalues in the range of 0-1 that represent the probability of a givenpixel being part of the cell's mask. The output images were thresholdedusing a value of 0.5 to obtain the ultimate binary mask. In case some ofthe masks intersect with each other, the non-maximum suppressionalgorithm was run (e.g., which is described in, for example, NavaneethBodla, “Improving Object Detection With One Line of Code,”arXiv:1704.04503v2 (August, 2017), available atarxiv.org/pdf/1704.04503.pdf, which is hereby incorporated by referenceherein in its entirety). For example, windows of prediction may includeredundant data, such as due to a cell in-between window edges. Such anintersection was avoided by processing only cells from the center, topand right corners of each image (e.g., such that the down and rightparts of the image are processed only if the current window is the lastwindow from the right or down side). The resulting cell segments werethen represented as the final output mask with integer valuesrepresenting individual cell instances, which were converted into cellcontours.

The training data set included 212 images that were cropped for trainingwith a cropping size of 300×300 pixels. All images were augmented,including dropout, rotation, elastic transformations, Gaussian blur,contrast adjustment, addition of Gaussian noise, decreasing membranechannels intensity (sometimes to zero), reflexion/reflection, andfurther cropped to a fixed size of 256×256 pixels. For the firstchannel, the nuclei marker was DAPI, for the second channel CD45 orNaKATPase, and various additional markers for the third channel(including CD16, CD163, CD45 (if the marker in second channel isNaKATPase), CD206, CD8, CD11c, CD20, CD56, and CD4). All labels wereacquired through human annotation. The Mask R-CNN model was trained onrandom crops of annotated images using backpropagation and an Adamoptimizer.

In FIG. 52, image 5202A shows first ground truth cell segmentationinformation for the first tissue sample. In these examples, sincesegmentation predicts contours without necessarily including cellborders, the cell segments could be close to each other pixel to pixel(or overlapping), which can make it difficult to see different segmentedcells in image 5202A. Therefore, the image 5202B shows different cellsegments of the first ground truth data shaded using different shadingsto help illustrate the various segmented cells. Image 5204A shows thedetermined cell segmentation information for the first ground truth datausing CellProfiler, and image 5204B shows the different segmented cellsdetermined by the cell profiler with shading to help illustrate thevarious segmented cells. Image 5206A shows the cell location informationfor the first ground truth data determined using Mask R-CNN, and image5206B shows the different segmented cells determined for the firstground truth data with shading.

Table 1 below shows comparison metrics for the CellProfiler and MaskR-CNN segmentations with the ground truth data for FIG. 52:

TABLE 1 PQ F1 Precision Recall Jaccard index Cell Profiler 0.37 0.570.66 0.5 0.4 Mask R-CNN 0.53 0.77 0.81 0.73 0.55

The Panoptic Quality (PQ) metric is used as a metric for performance,and is described in, for example, Alexander Kirillov, “PanopticSegmentation,” arXiv:1801.00868 (April, 2019), which is available atarxiv.org/abs/1801.00868.

The Jaccard index shows cell detection accuracy and cell shapereproduction accuracy. The value is determined by matching cells betweenthe ground truth data and the cell segmentation prediction using theHungarian algorithm (where each cell has either zero or oneconnections). For each matched pair, the Jaccard index is calculatedbetween them, and the final result is computed as a sum of the valuesdivided by the maximum number of cells (the number of cells in theground truth mask or in the prediction mask).

F1, Precision, and Recall are metrics used in machine learning to checkthe quality of prediction compared with some ground truth (GT) objects(or segments, in these examples), where Precision=TPS/GTS;Recall=TPS/PS, and F1=2*Precision*Recall/(Precision+Recall). TPS (TruePositive Segments) represents the number of segments from a neuralnetwork prediction that have a reference segment from the GT withJaccard>=0.5, GTS is the number of all segments from GT, and PS is thenumber of all segments from the prediction.

In FIG. 53, image 5302A shows second ground truth cell segmentationinformation for the second tissue sample, and image 5302B shows thedifferent cell segmentations of the second ground truth data usingshading. Image 5304A shows the determined cell segmentation informationfor the second ground truth data using CellProfiler, and image 5304Bshows the different segmented cells determined via CellProfiler withshading. Image 5306A shows the cell segmentation information determinedfor the second ground truth data using Mask R-CNN, and image 5306B showsthe different segmented cells determined for the second ground truthdata using Mask R-CNN with shading.

Table 2 below shows comparison metrics for the CellProfiler and MaskR-CNN segmentations with the ground truth data for FIG. 53:

TABLE 2 PQ F1 Precision Recall Jaccard index Cell Profiler 0.22 0.360.43 0.31 0.32 Mask R-CNN 0.45 0.67 0.79 0.59 0.45

As shown by the images in FIGS. 52-53 and the comparison data in Tables1 and 2, Mask R-CNN had better cell contour detection than CellProfiler,since the model was trained for cell segmentation (while CellProfilergenerally determines the cell segmentation based on nuclei thresholding,and cell membrane identification by expanding nuclei as outlined by userimputed number of pixels). Mask R-CNN produced less false celldetections than CellProfiler (e.g., as shown by the higher F1 and PQmetrics), and reproduced shapes of cells truer to real cell form thanCellProfiler (e.g., as shown by the higher PQ and Jaccard indexmetrics). Therefore, Mask R-CNN demonstrates significant improvementfrom CellProfiler and other similar conventional approaches for cellsegmentation. Such improved cell segmentation can, in-turn, improvesubsequent aspects performed by the image processing pipeline thatleverage the cell segmentation, since further analysis can use and/ordepend on the cell segmentation data as described herein.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of processor-executableinstructions that can be employed to program a computer or otherprocessor (physical or virtual) to implement various aspects ofembodiments as discussed above. Additionally, according to one aspect,one or more computer programs that when executed perform methods of thedisclosure provided herein need not reside on a single computer orprocessor, but may be distributed in a modular fashion among differentcomputers or processors to implement various aspects of the disclosureprovided herein.

Processor-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed.

Also, data structures may be stored in one or more non-transitorycomputer-readable storage media in any suitable form. For simplicity ofillustration, data structures may be shown to have fields that arerelated through location in the data structure. Such relationships maylikewise be achieved by assigning storage for the fields with locationsin a non-transitory computer-readable medium that convey relationshipbetween the fields. However, any suitable mechanism may be used toestablish relationships among information in fields of a data structure,including through the use of pointers, tags or other mechanisms thatestablish relationships among data elements.

Various inventive concepts may be embodied as one or more processes, ofwhich examples have been provided. The acts performed as part of eachprocess may be ordered in any suitable way. Thus, embodiments may beconstructed in which acts are performed in an order different thanillustrated, which may include performing some acts simultaneously, eventhough shown as sequential acts in illustrative embodiments.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, forexample, “at least one of A and B” (or, equivalently, “at least one of Aor B,” or, equivalently “at least one of A and/or B”) can refer, in oneembodiment, to at least one, optionally including more than one, A, withno B present (and optionally including elements other than B); inanother embodiment, to at least one, optionally including more than one,B, with no A present (and optionally including elements other than A);in yet another embodiment, to at least one, optionally including morethan one, A, and at least one, optionally including more than one, B(and optionally including other elements); etc.

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, i.e., elements that are conjunctively present in some casesand disjunctively present in other cases. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined. Other elements may optionally be presentother than the elements specifically identified by the “and/or” clause,whether related or unrelated to those elements specifically identified.Thus, as a non-limiting example, a reference to “A and/or B”, when usedin conjunction with open-ended language such as “comprising” can refer,in one embodiment, to A only (optionally including elements other thanB); in another embodiment, to B only (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Such terms areused merely as labels to distinguish one claim element having a certainname from another element having a same name (but for use of the ordinalterm). The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” “having,” “containing”, “involving”, andvariations thereof, is meant to encompass the items listed thereafterand additional items.

Having described several embodiments of the techniques described hereinin detail, various modifications, and improvements will readily occur tothose skilled in the art. Such modifications and improvements areintended to be within the spirit and scope of the disclosure.Accordingly, the foregoing description is by way of example only, and isnot intended as limiting. The techniques are limited only as defined bythe following claims and the equivalents thereto.

What is claimed is:
 1. A method, comprising: using at least one computerhardware processor to perform: obtaining at least one multiplexedimmunofluorescence (MxIF) image of a same tissue sample, wherein the atleast one MxIF image comprises a plurality of immunofluorescence images;obtaining, using a machine learning technique, information indicative oflocations of cells in the at least one MxIF image, the obtainingcomprising analyzing the plurality of immunofluorescence images toidentify one or more damaged portions of the tissue sample, theanalyzing comprising: inputting a corresponding portion of each of atleast two of the plurality of immunofluorescence images to a firsttrained neural network configured to identify differences betweenimmunofluorescence images, wherein the at least two immunofluorescenceimages comprise images of a same marker; and obtaining, from the firsttrained neural network, at least one classification of the portion ofthe tissue sample; identifying multiple groups of cells in the at leastone MxIF image at least in part by: determining feature values for atleast some of the cells using the at least one MxIF image and theinformation indicative of locations of cells in the at least one MxIFimage; and grouping the at least some of the cells into the multiplegroups using the determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.
 2. Themethod of claim 1, wherein the at least some of the cells include afirst cell, and wherein determining the feature values comprisesdetermining first feature values for the first cell using at least onepixel value associated with a location of the first cell in the at leastone MxIF image.
 3. The method of claim 2, wherein determining the firstfeature values for the first cell comprises using pixel valuesassociated with respective locations of the first cell in multiplechannels of the at least one MxIF image.
 4. The method of claim 3,wherein: the multiple channels are associated with respective markers ina plurality of markers; determining the first feature values using thepixel values associated with the respective locations of the first cellin the multiple channels of the at least one MxIF image comprisesdetermining a marker expression signature that includes, for eachparticular marker of one or more of the plurality of markers, alikelihood that the particular marker is expressed in the first cell;and grouping the at least some of the cells into the multiple groupscomprises: determining a predicted cell type for the first cell usingthe marker expression signature and cell typing data; and associatingthe first cell with one of the multiple groups based on the predictedcell type.
 5. The method of claim 4, wherein: the cell typing datacomprises at least one marker expression signature for each of aplurality of cell types; and the at least one marker expressionsignature for each particular cell type of the plurality of cell typescomprises data indicative of which of the plurality of markers isexpressed in cells of the particular cell type.
 6. The method of claim5, wherein determining the predicted cell type of the first cellcomprises comparing the marker expression signature of the first cellwith the at least one marker expression signature for each of theplurality of cell types to determine the predicted cell type.
 7. Themethod of claim 4, further comprising: providing, as input to a secondtrained neural network configured to determine marker expressionsignatures: (a) the at least one MxIF image and (b) information, fromthe information indicative of the locations of the cells, indicative ofa location of the first cell in at least some of the multiple channels;and obtaining the marker expression signature as output from the secondtrained neural network.
 8. The method of claim 3, wherein using pixelvalues associated with respective locations of the first cell in themultiple channels comprises, for each of the multiple channels:identifying a set of pixels for the first cell using the location of thefirst cell in the channel; and determining a feature value of the firstfeature values for the first cell based on values of pixels in the setof pixels by determining an average pixel intensity of the values of thepixels in the set of pixels.
 9. The method of claim 1, whereindetermining the at least one characteristic comprises determininginformation about cell types in the tissue sample.
 10. The method ofclaim 9, wherein determining the information about the cell typescomprises identifying cell types of individual cells of the at leastsome of the cells in the at least one MxIF image, wherein the cell typescomprise one or more of endothelial cells, epithelial cells,macrophages, T cells, malignant cells, NK cells, B cells, and acinicells.
 11. The method of claim 1, wherein determining the at least onecharacteristic comprises determining at least one of: an acini maskindicating locations of acini in the at least one MxIF image; a stromalmask indicating locations of stroma in the at least one MxIF image; atumor mask indicating locations of a tumor in the at least one MxIFimage; and a cell cluster mask indicating locations of one or more cellclusters in the at least one MxIF image.
 12. The method of claim 1,wherein determining the at least one characteristic comprisesdetermining one or more cell clusters in the tissue sample based on themultiple groups comprising: determining a first set of cell features foreach of the at least some cells by generating a graph comprising a nodefor each of the at least some cells, and edges between the nodes; anddetermining the one or more cell clusters based on the first set of cellfeatures of the at least some cells.
 13. The method of claim 12, whereindetermining the one or more cell clusters in the tissue samplecomprises: providing the graph as input to a graph neural network toobtain embedded features in a latent space; clustering the embeddedfeatures to obtain clusters of the nodes; and using the clusters of thenodes to determine the one or more cell clusters.
 14. The method ofclaim 12, further comprising determining the first set of cell featuresfor each node of the graph based on a group of the multiple groups thatincludes the cell, lengths of edges of the node in the graph, and maskdata.
 15. The method of claim 13, wherein the graph neural networkcomprises one or more convolutional layers including at least a lastgraph convolutional layer, and the embedded features are generated basedon activations of the last graph convolutional layer.
 16. The method ofclaim 1, wherein obtaining the at least one MxIF image of the tissuesample comprises: performing background subtraction on a first channelof the at least one MxIF image.
 17. The method of claim 16, whereinperforming the background subtraction comprises providing the firstchannel as input to a third trained neural network configured to performbackground subtraction, wherein the third trained neural networkcomprises at least one million parameters.
 18. The method of claim 1,wherein the at least one classification comprises a set ofclassifications comprising a first classification of no cells, a secondclassification of undamaged cells and/or a third classification ofdamaged cells; wherein the set of classifications comprises, for eachclassification, an associated confidence of the classification applyingto the portion; and selecting, based on the confidences, a finalclassification from the set of classifications for the portion.
 19. Themethod of claim 18, further comprising: generating a patch maskindicating the final classification of the portion and finalclassifications for a plurality of other portions of the at least twoimmunofluorescence images; removing, based on the patch mask, a portionof a segmentation mask for the tissue sample so that cells of the tissuesample associated with the removed portion are not included in themultiple groups.
 20. The method of claim 1, wherein obtaining theinformation indicative of the locations of the cells in the at least oneMxIF image using the machine learning technique comprises: applying afourth trained neural network to at least one channel of the at leastone MxIF image to generate the information indicative of the locationsof the cells.
 21. The method of claim 20, wherein: the fourth trainedneural network is implemented using a U-Net architecture or aregion-based convolutional neural network architecture; and the fourthtrained neural network comprises at least one million parameters. 22.The method of claim 20, further comprising: training the fourth trainedneural network using a set of training immunofluorescence images oftissue samples as input images and associated output images comprisinginformation indicative of locations of cells in the input images. 23.The method of claim 1, wherein grouping the at least some of the cellsinto the multiple groups comprises performing cell clustering to groupthe at least some of the cells into the multiple groups based on thedetermined feature values.
 24. The method of claim 1, wherein groupingthe at least some of the cells into the multiple groups comprises:analyzing the determined feature values to determine relationships amongthe at least some of the cells; and determining the multiple groupsbased on the determined relationships such that each cell in a group ofthe multiple groups has feature values that are indicative of arelationship among cells in the group.
 25. A system, comprising: atleast one computer hardware processor; and at least one non-transitorycomputer-readable storage medium storing processor-executableinstructions that, when executed by the at least one computer hardwareprocessor, cause the at least one computer hardware processor toperform: obtaining at least one multiplexed immunofluorescence (MxIF)image of a same tissue sample, wherein the at least one MxIF imagecomprises a plurality of immunofluorescence images; obtaining, using amachine leaning technique, information indicative of locations of cellsin the at least one MxIF image, the obtaining comprising analyzing theplurality of immunofluorescence images to identify one or more damagedportions of the tissue sample, the analyzing comprising: inputting acorresponding portion of each of at least two of the plurality ofimmunofluorescence images to a first trained neural network configuredto identify differences between immunofluorescence images, wherein theat least two immunofluorescence images comprise images of a same marker;and obtaining, from the first trained neural network, at least oneclassification of the portion of the tissue sample; identifying multiplegroups of cells in the at least one MxIF image at least in part by:determining feature values for at least some of the cells using the atleast one MxIF image and the information indicative of locations ofcells; and grouping the at least some of the cells into the multiplegroups using the determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.
 26. Atleast one non-transitory computer-readable storage medium storingprocessor-executable instructions that, when executed by at least onecomputer hardware processor, cause the at least one computer hardwareprocessor to perform: obtaining at least one multiplexedimmunofluorescence (MxIF) image of a same tissue sample, wherein the atleast one MxIF image comprises a plurality of immunofluorescence images;obtaining, using a machine leaning technique, information indicative oflocations of cells in the at least one MxIF image, the obtainingcomprising analyzing the plurality of immunofluorescence images toidentify one or more damaged portions of the tissue sample, theanalyzing comprising: inputting a corresponding portion of each of atleast two of the plurality of immunofluorescence images to a firsttrained neural network configured to identify differences betweenimmunofluorescence images, wherein the at least two immunofluorescenceimages comprise images of a same marker; and obtaining, from the firsttrained neural network, at least one classification of the portion ofthe tissue sample; identifying multiple groups of cells in the at leastone MxIF image at least in part by: determining feature values for atleast some of the cells using the at least one MxIF image and theinformation indicative of locations of cells in the at least one MxIFimage; and grouping the at least some of the cells into the multiplegroups using the determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.
 27. Amethod, comprising: using at least one computer hardware processor toperform: obtaining at least one multiplexed immunofluorescence (MxIF)image of a tissue sample, wherein the at least one MxIF image comprisesa plurality of immunofluorescence images; obtaining, using a machinelearning technique, information indicative of a location of at least onecell in the at least one MxIF image, the obtaining comprising analyzingthe plurality of immunofluorescence images to identify one or moredamaged portions of the tissue sample, the analyzing comprising:inputting a corresponding portion of each of at least two of theplurality of immunofluorescence images to a first trained neural networkconfigured to identify differences between immunofluorescence images,wherein the at least two immunofluorescence images comprise images of asame marker; and obtaining, from the first trained neural network, atleast one classification of the portion of the tissue sample;determining a marker expression signature for the at least one cell inthe tissue sample based on a plurality of markers expressed in the atleast one MxIF image and the information indicative of the location ofthe at least one cell in the at least one MxIF image; and comparing themarker expression signature to cell typing data that comprises at leastone marker expression signature for a plurality of different types ofcells to determine a cell type for the at least one cell.
 28. The methodof claim 27, further comprising determining at least one characteristicof the tissue sample based on the cell type of the at least one cell.29. A method, comprising: using at least one computer hardware processorto perform: obtaining at least one multiplexed immunofluorescence (MxIF)image of a same tissue sample, the obtaining comprising performingbackground subtraction on a first channel of the at least one MxIFimage, wherein performing the background subtraction comprises providingthe first channel as input to a trained neural network configured toperform background subtraction, wherein the trained neural networkcomprises at least one million parameters; obtaining, using a machinelearning technique, information indicative of locations of cells in theat least one MxIF image; identifying multiple groups of cells in the atleast one MxIF image at least in part by: determining feature values forat least some of the cells using the at least one MxIF image and theinformation indicative of locations of cells in the at least one MxIFimage; and grouping the at least some of the cells into the multiplegroups using the determined feature values; and determining at least onecharacteristic of the tissue sample using the multiple groups.
 30. Asystem, comprising: at least one computer hardware processor; and atleast one non-transitory computer-readable storage medium storingprocessor-executable instructions that, when executed by the at leastone computer hardware processor, cause the at least one computerhardware processor to perform: obtaining at least one multiplexedimmunofluorescence (MxIF) image of a same tissue sample, the obtainingcomprising performing background subtraction on a first channel of theat least one MxIF image, wherein performing the background subtractioncomprises providing the first channel as input to a trained neuralnetwork configured to perform background subtraction, wherein thetrained neural network comprises at least one million parameters;obtaining, using a machine learning technique, information indicative oflocations of cells in the at least one MxIF image; identifying multiplegroups of cells in the at least one MxIF image at least in part by:determining feature values for at least some of the cells using the atleast one MxIF image and the information indicative of locations ofcells in the at least one MxIF image; and grouping the at least some ofthe cells into the multiple groups using the determined feature values;and determining at least one characteristic of the tissue sample usingthe multiple groups.
 31. At least one non-transitory computer-readablestorage medium storing processor-executable instructions that, whenexecuted by at least one computer hardware processor, cause the at leastone computer hardware processor to perform: obtaining at least onemultiplexed immunofluorescence (MxIF) image of a same tissue sample, theobtaining comprising performing background subtraction on a firstchannel of the at least one MxIF image, wherein performing thebackground subtraction comprises providing the first channel as input toa trained neural network configured to perform background subtraction,wherein the trained neural network comprises at least one millionparameters; obtaining, using a machine learning technique, informationindicative of locations of cells in the at least one MxIF image;identifying multiple groups of cells in the at least one MxIF image atleast in part by: determining feature values for at least some of thecells using the at least one MxIF image and the information indicativeof locations of cells in the at least one MxIF image; and grouping theat least some of the cells into the multiple groups using the determinedfeature values; and determining at least one characteristic of thetissue sample using the multiple groups.